{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bbb9de38-5318-4e53-a2aa-409c7b991fe8",
   "metadata": {},
   "source": [
    "# ARIMA and SARIMAX\n",
    "\n",
    "ARIMA (AutoRegressive Integrated Moving Average) and SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous estimators) are prominent and widely used statistical forecasting models. While ARIMA models are more widely known, SARIMAX models extend the ARIMA framework by seamlessly integrating seasonal patterns and exogenous variables.\n",
    "\n",
    "In the ARIMA-SARIMAX model notation, the parameters $p$, $d$, and $q$ represent the autoregressive, differencing, and moving-average components, respectively. $P$, $D$, and $Q$ denote the same components for the seasonal part of the model, with $m$ representing the number of periods in each season.\n",
    "\n",
    "+ $p$ is the order (number of time lags) of the autoregressive part of the model.\n",
    "\n",
    "+ $d$ is the degree of differencing (the number of times that past values have been subtracted from the data).\n",
    "\n",
    "+ $q$ is the order of the moving average part of the model.\n",
    "\n",
    "+ $P$ is the order (number of time lags) of the seasonal part of the model.\n",
    "\n",
    "+ $D$ is the degree of differencing (the number of times the data have had past values subtracted) of the seasonal part of the model.\n",
    "\n",
    "+ $Q$ is the order of the moving average of the seasonal part of the model.\n",
    "\n",
    "+ $m$ refers to the number of periods in each season.\n",
    "\n",
    "When the terms $P$, $D$, $Q$, and $m$ are zero and no exogenous variables are included in the model, the SARIMAX model is equivalent to an ARIMA.\n",
    "\n",
    "When two out of the three terms are zero, the model can be referred to based on the non-zero parameter, dropping \"AR\", \"I\" or \"MA\" from the acronym describing the model. For example, $ARIMA(1,0,0)$ is $AR(1)$, $ARIMA(0,1,0)$ is $I(1)$, and $ARIMA(0,0,1)$ is $MA(1)$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c3a970e",
   "metadata": {},
   "source": [
    "<font size=\"6\">ARIMA implementations</font>\n",
    "\n",
    "Several Python libraries implement ARIMA-SARIMAX models. Three of them are:\n",
    "\n",
    "+ [statsmodels](https://www.statsmodels.org/stable/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html): this is one of the most complete libraries for statistical modeling. While the functional paradigm may be intuitive for those coming from the R environment, those accustomed to the object-oriented API of scikit-learn may need a short period of adaptation.\n",
    "\n",
    "+ [skforecast](../api/stats.html#skforecast.stats._sarimax.Sarimax): a novel wrapper for `statsmodels SARIMAX` that also follows the scikit-learn API. This implementation is very similar to that of pmdarima, but has been streamlined to include only the essential elements for skforecast, resulting in significant speed improvements.\n",
    "\n",
    "+ [aeon](https://www.aeon-toolkit.org/en/stable/api_reference/auto_generated/aeon.forecasting.stats.ARIMA.html#arima): althought still in early development, aeon includes a super fast implementation of ARIMA that follows the scikit-learn API."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a18a01e",
   "metadata": {},
   "source": [
    "<font size=\"6\">ForecasterStats</font>\n",
    "\n",
    "The [`ForecasterStats`](..api/forecasterstats.html) class allows training and validation of ARIMA and SARIMAX models using the skforecast API. `ForecasterStats` is compatible with the [`Sarimax` from skforecast](../api/stats.html#skforecast.stats._sarimax.Sarimax) and the [`ARIMA` from aeon](https://www.aeon-toolkit.org/en/stable/api_reference/auto_generated/aeon.forecasting.stats.ARIMA.html#arima) implementations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ca6b79c",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,191,191,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00bfa5; border-color: #00bfa5; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00bfa5;\"></i>\n",
    "    <b style=\"color: #00bfa5;\">&#128161 Tip</b>\n",
    "</p>\n",
    "\n",
    "<p>\n",
    "To learn more about modeling time series with statistical models, visit our examples:\n",
    "\n",
    "<ul>\n",
    "    <li><a href=\"https://www.cienciadedatos.net/documentos/py51-arima-sarimax-models-python.html\">ARIMA and SARIMAX models with Python</a></li>\n",
    "    <li><a href=\"https://cienciadedatos.net/documentos/py73-arar-forecasting-models-python\">ARAR models with Python</a></li>\n",
    "    <li><a href=\"../faq/benchmarking-statistical-models.html\">Benchmark of statistical models for forecasting</a></li>\n",
    "</ul>\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a20a0532",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(255,145,0,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #ff9100; border-color: #ff9100; padding-left: 10px; padding-right: 10px\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#ff9100; border-color: #ff1744;\"></i>\n",
    "    <b style=\"color: #ff9100;\"> <span style=\"color: #ff9100;\">&#9888;</span> Warning</b>\n",
    "</p>\n",
    "\n",
    "<p>\n",
    "Since version <b>0.19.0</b> of skforecast, the class <code>Sarimax</code> has been moved from <code>skforecast.sarimax</code> to <code>skforecast.stats</code>. Please update your imports to use <code>from skforecast.stats import Sarimax</code>.\n",
    "</p>\n",
    "<p>\n",
    "Since version <b>0.19.0</b> of skforecast, the class <code>ForecasterSarimax</code> has been deprecated in favor of the new class <code>ForecasterStats</code>, which provides improved performance and broader compatibility with statistical models. Users are encouraged to transition to <code>ForecasterStats</code> for future projects.\n",
    "\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d1ae70e1-1354-4aa6-9dc4-73d3c7b016c5",
   "metadata": {},
   "source": [
    "## Libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d378f5ff-8092-49bb-80b7-8a3996d19fb1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-06T18:53:26.295308Z",
     "start_time": "2022-03-06T18:53:25.182680Z"
    }
   },
   "outputs": [],
   "source": [
    "# Libraries\n",
    "# ==============================================================================\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from skforecast.datasets import fetch_dataset\n",
    "from skforecast.stats import Sarimax\n",
    "from skforecast.recursive import ForecasterStats\n",
    "from skforecast.model_selection import TimeSeriesFold, backtesting_stats, grid_search_stats\n",
    "from skforecast.plot import set_dark_theme\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b59e2b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭─────────────────────────────────── <span style=\"font-weight: bold\">h2o_exog</span> ────────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                    │\n",
       "│ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health   │\n",
       "│ system had between 1991 and 2008. Two additional variables (exog_1, exog_2) are │\n",
       "│ simulated.                                                                      │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                         │\n",
       "│ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice (3rd      │\n",
       "│ Edition). http://pkg.robjhyndman.com/fpp3package/,                              │\n",
       "│ https://github.com/robjhyndman/fpp3package, http://OTexts.com/fpp3.             │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/h2o_exog.csv                                                 │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Shape:</span> 195 rows x 3 columns                                                     │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭─────────────────────────────────── \u001b[1mh2o_exog\u001b[0m ────────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                    │\n",
       "│ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health   │\n",
       "│ system had between 1991 and 2008. Two additional variables (exog_1, exog_2) are │\n",
       "│ simulated.                                                                      │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                         │\n",
       "│ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice (3rd      │\n",
       "│ Edition). http://pkg.robjhyndman.com/fpp3package/,                              │\n",
       "│ https://github.com/robjhyndman/fpp3package, http://OTexts.com/fpp3.             │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/h2o_exog.csv                                                 │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mShape:\u001b[0m 195 rows x 3 columns                                                     │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Download data\n",
    "# ==============================================================================\n",
    "data = fetch_dataset(name=\"h2o_exog\")\n",
    "data.index.name = 'datetime'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fe2cb76c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dates : 1992-04-01 00:00:00 --- 2005-06-01 00:00:00  (n=159)\n",
      "Test dates  : 2005-07-01 00:00:00 --- 2008-06-01 00:00:00  (n=36)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train-test dates\n",
    "# ==============================================================================\n",
    "end_train = '2005-06-01 23:59:59'\n",
    "print(\n",
    "    f\"Train dates : {data.index.min()} --- {data.loc[:end_train].index.max()}  \"\n",
    "    f\"(n={len(data.loc[:end_train])})\"\n",
    ")\n",
    "print(\n",
    "    f\"Test dates  : {data.loc[end_train:].index.min()} --- {data.loc[:].index.max()}  \"\n",
    "    f\"(n={len(data.loc[end_train:])})\"\n",
    ")\n",
    "data_train = data.loc[:end_train]\n",
    "data_test  = data.loc[end_train:]\n",
    "\n",
    "# Plot\n",
    "# ==============================================================================\n",
    "set_dark_theme()\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "data.plot(ax=ax)\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdaca6ff",
   "metadata": {},
   "source": [
    "## Statsmodels and skforecast"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c64ebb48",
   "metadata": {},
   "source": [
    "The following section focus on how to train an ARIMA model and forecast future values with each of the three libraries."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33edbfec",
   "metadata": {},
   "source": [
    "<font size=\"5\">**statsmodels**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27ae794e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>159</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 1)</td> <th>  Log Likelihood     </th>  <td>89.934</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 26 Nov 2025</td> <th>  AIC                </th> <td>-173.869</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>15:05:55</td>     <th>  BIC                </th> <td>-164.681</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>04-01-1992</td>    <th>  HQIC               </th> <td>-170.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 06-01-2005</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    0.6316</td> <td>    0.143</td> <td>    4.420</td> <td> 0.000</td> <td>    0.352</td> <td>    0.912</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.9534</td> <td>    0.054</td> <td>  -17.814</td> <td> 0.000</td> <td>   -1.058</td> <td>   -0.849</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.0186</td> <td>    0.002</td> <td>    8.619</td> <td> 0.000</td> <td>    0.014</td> <td>    0.023</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.75</td> <th>  Jarque-Bera (JB):  </th> <td>167.05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.39</td> <th>  Prob(JB):          </th>  <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>2.13</td> <th>  Skew:              </th>  <td>-1.66</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.01</td> <th>  Kurtosis:          </th>  <td>6.78</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &        y         & \\textbf{  No. Observations:  } &    159      \\\\\n",
       "\\textbf{Model:}                  & SARIMAX(1, 1, 1) & \\textbf{  Log Likelihood     } &   89.934    \\\\\n",
       "\\textbf{Date:}                   & Wed, 26 Nov 2025 & \\textbf{  AIC                } &  -173.869   \\\\\n",
       "\\textbf{Time:}                   &     15:05:55     & \\textbf{  BIC                } &  -164.681   \\\\\n",
       "\\textbf{Sample:}                 &    04-01-1992    & \\textbf{  HQIC               } &  -170.137   \\\\\n",
       "\\textbf{}                        &   - 06-01-2005   & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &       opg        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{ar.L1}  &       0.6316  &        0.143     &     4.420  &         0.000        &        0.352    &        0.912     \\\\\n",
       "\\textbf{ma.L1}  &      -0.9534  &        0.054     &   -17.814  &         0.000        &       -1.058    &       -0.849     \\\\\n",
       "\\textbf{sigma2} &       0.0186  &        0.002     &     8.619  &         0.000        &        0.014    &        0.023     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 0.75 & \\textbf{  Jarque-Bera (JB):  } & 167.05  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.39 & \\textbf{  Prob(JB):          } &  0.00   \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 2.13 & \\textbf{  Skew:              } & -1.66   \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.01 & \\textbf{  Kurtosis:          } &  6.78   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  159\n",
       "Model:               SARIMAX(1, 1, 1)   Log Likelihood                  89.934\n",
       "Date:                Wed, 26 Nov 2025   AIC                           -173.869\n",
       "Time:                        15:05:55   BIC                           -164.681\n",
       "Sample:                    04-01-1992   HQIC                          -170.137\n",
       "                         - 06-01-2005                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.6316      0.143      4.420      0.000       0.352       0.912\n",
       "ma.L1         -0.9534      0.054    -17.814      0.000      -1.058      -0.849\n",
       "sigma2         0.0186      0.002      8.619      0.000       0.014       0.023\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.75   Jarque-Bera (JB):               167.05\n",
       "Prob(Q):                              0.39   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               2.13   Skew:                            -1.66\n",
       "Prob(H) (two-sided):                  0.01   Kurtosis:                         6.78\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ARIMA model with statsmodels.Sarimax\n",
    "# ==============================================================================\n",
    "arima = SARIMAX(endog = data_train['y'], order = (1, 1, 1))\n",
    "arima_res = arima.fit(disp=0)\n",
    "arima_res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e6d534cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2005-07-01    0.859455\n",
       "2005-08-01    0.870313\n",
       "2005-09-01    0.877172\n",
       "2005-10-01    0.881503\n",
       "Freq: MS, Name: predicted_mean, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction\n",
    "# ==============================================================================\n",
    "predictions = arima_res.get_forecast(steps=12)\n",
    "predictions.predicted_mean.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "463e7aeb",
   "metadata": {},
   "source": [
    "<font size=\"5\">**skforecast**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e612dc13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>159</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 1)</td> <th>  Log Likelihood     </th>  <td>89.934</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 26 Nov 2025</td> <th>  AIC                </th> <td>-173.869</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>15:05:55</td>     <th>  BIC                </th> <td>-164.681</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>04-01-1992</td>    <th>  HQIC               </th> <td>-170.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 06-01-2005</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    0.6316</td> <td>    0.143</td> <td>    4.420</td> <td> 0.000</td> <td>    0.352</td> <td>    0.912</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.9534</td> <td>    0.054</td> <td>  -17.814</td> <td> 0.000</td> <td>   -1.058</td> <td>   -0.849</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.0186</td> <td>    0.002</td> <td>    8.619</td> <td> 0.000</td> <td>    0.014</td> <td>    0.023</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.75</td> <th>  Jarque-Bera (JB):  </th> <td>167.05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.39</td> <th>  Prob(JB):          </th>  <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>2.13</td> <th>  Skew:              </th>  <td>-1.66</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.01</td> <th>  Kurtosis:          </th>  <td>6.78</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &        y         & \\textbf{  No. Observations:  } &    159      \\\\\n",
       "\\textbf{Model:}                  & SARIMAX(1, 1, 1) & \\textbf{  Log Likelihood     } &   89.934    \\\\\n",
       "\\textbf{Date:}                   & Wed, 26 Nov 2025 & \\textbf{  AIC                } &  -173.869   \\\\\n",
       "\\textbf{Time:}                   &     15:05:55     & \\textbf{  BIC                } &  -164.681   \\\\\n",
       "\\textbf{Sample:}                 &    04-01-1992    & \\textbf{  HQIC               } &  -170.137   \\\\\n",
       "\\textbf{}                        &   - 06-01-2005   & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &       opg        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{ar.L1}  &       0.6316  &        0.143     &     4.420  &         0.000        &        0.352    &        0.912     \\\\\n",
       "\\textbf{ma.L1}  &      -0.9534  &        0.054     &   -17.814  &         0.000        &       -1.058    &       -0.849     \\\\\n",
       "\\textbf{sigma2} &       0.0186  &        0.002     &     8.619  &         0.000        &        0.014    &        0.023     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 0.75 & \\textbf{  Jarque-Bera (JB):  } & 167.05  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.39 & \\textbf{  Prob(JB):          } &  0.00   \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 2.13 & \\textbf{  Skew:              } & -1.66   \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.01 & \\textbf{  Kurtosis:          } &  6.78   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  159\n",
       "Model:               SARIMAX(1, 1, 1)   Log Likelihood                  89.934\n",
       "Date:                Wed, 26 Nov 2025   AIC                           -173.869\n",
       "Time:                        15:05:55   BIC                           -164.681\n",
       "Sample:                    04-01-1992   HQIC                          -170.137\n",
       "                         - 06-01-2005                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.6316      0.143      4.420      0.000       0.352       0.912\n",
       "ma.L1         -0.9534      0.054    -17.814      0.000      -1.058      -0.849\n",
       "sigma2         0.0186      0.002      8.619      0.000       0.014       0.023\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.75   Jarque-Bera (JB):               167.05\n",
       "Prob(Q):                              0.39   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               2.13   Skew:                            -1.66\n",
       "Prob(H) (two-sided):                  0.01   Kurtosis:                         6.78\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ARIMA model with skforecast.Sarimax\n",
    "# ==============================================================================\n",
    "arima = Sarimax(order=(1, 1, 1))\n",
    "arima.fit(y=data_train['y'])\n",
    "arima.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5440040e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-07-01</th>\n",
       "      <td>0.859455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-08-01</th>\n",
       "      <td>0.870313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-09-01</th>\n",
       "      <td>0.877172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-10-01</th>\n",
       "      <td>0.881503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                pred\n",
       "2005-07-01  0.859455\n",
       "2005-08-01  0.870313\n",
       "2005-09-01  0.877172\n",
       "2005-10-01  0.881503"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction\n",
    "# ==============================================================================\n",
    "predictions = arima.predict(steps=12)\n",
    "predictions.head(4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9801a209-b601-4d9b-aa3c-a2e03df98c98",
   "metadata": {},
   "source": [
    "## ForecasterStats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b159ce",
   "metadata": {},
   "source": [
    "The previous section introduced the construction of ARIMA-SARIMAX models using three different implementations. In order to seamlessly integrate these models with the various functionalities provided by **skforecast**, the next step is to encapsulate the skforecast [`Sarimax`](../api/stats.html#skforecast.stats._sarimax.Sarimax) model within a [`ForecasterStats`](..api/forecasterstats.html) object. This encapsulation harmonizes the intricacies of the model and allows for the coherent use of skforecast's extensive capabilities."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4c8e5dca",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4f83164f-9758-485e-86bb-05bbbc04fccd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-06T18:53:34.987954Z",
     "start_time": "2022-03-06T18:53:30.218865Z"
    }
   },
   "outputs": [
    {
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       "    </style>\n",
       "    \n",
       "        <div class=\"container-1b8c458f1b124dbbb52fcbdb4f3e6d08\">\n",
       "            <p style=\"font-size: 1.5em; font-weight: bold; margin-block-start: 0.83em; margin-block-end: 0.83em;\">ForecasterStats</p>\n",
       "            <details open>\n",
       "                <summary>General Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Estimator:</strong> Sarimax</li>\n",
       "                    <li><strong>Window size:</strong> 1</li>\n",
       "                    <li><strong>Series name:</strong> y</li>\n",
       "                    <li><strong>Exogenous included:</strong> False</li>\n",
       "                    <li><strong>Creation date:</strong> 2025-11-26 15:05:55</li>\n",
       "                    <li><strong>Last fit date:</strong> 2025-11-26 15:05:57</li>\n",
       "                    <li><strong>Skforecast version:</strong> 0.19.0</li>\n",
       "                    <li><strong>Python version:</strong> 3.12.11</li>\n",
       "                    <li><strong>Forecaster id:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Exogenous Variables</summary>\n",
       "                <ul>\n",
       "                    None\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Data Transformations</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Transformer for y:</strong> None</li>\n",
       "                    <li><strong>Transformer for exog:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Training Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Training range:</strong> [Timestamp('1992-04-01 00:00:00'), Timestamp('2005-06-01 00:00:00')]</li>\n",
       "                    <li><strong>Training index type:</strong> DatetimeIndex</li>\n",
       "                    <li><strong>Training index frequency:</strong> MS</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Estimator Parameters</summary>\n",
       "                <ul>\n",
       "                    \n",
       "    {'concentrate_scale': False, 'dates': None, 'disp': False,\n",
       "    'enforce_invertibility': True, 'enforce_stationarity': True, 'freq': None,\n",
       "    'hamilton_representation': False, 'maxiter': 200, 'measurement_error':\n",
       "    False, 'method': 'lbfgs', 'missing': 'none', 'mle_regression': True,\n",
       "    'order': (12, 1, 1), 'seasonal_order': (0, 0, 0, 0), 'simple_differencing':\n",
       "    False, 'sm_fit_kwargs': {}, 'sm_init_kwargs': {}, 'sm_predict_kwargs': {},\n",
       "    'start_params': None, 'time_varying_regression': False, 'trend': None,\n",
       "    'trend_offset': 1, 'use_exact_diffuse': False, 'validate_specification':\n",
       "    True}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Fit Kwargs</summary>\n",
       "                <ul>\n",
       "                    {}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <p>\n",
       "                <a href=\"https://skforecast.org/0.19.0/api/forecasterstats.html\">&#128712 <strong>API Reference</strong></a>\n",
       "                &nbsp;&nbsp;\n",
       "                <a href=\"https://skforecast.org/0.19.0/user_guides/forecasting-sarimax-arima.html\">&#128462 <strong>User Guide</strong></a>\n",
       "            </p>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "=============== \n",
       "ForecasterStats \n",
       "=============== \n",
       "Estimator: Sarimax(12,1,1)(0,0,0)[0] \n",
       "Series name: y \n",
       "Exogenous included: False \n",
       "Exogenous names: None \n",
       "Transformer for y: None \n",
       "Transformer for exog: None \n",
       "Training range: [Timestamp('1992-04-01 00:00:00'), Timestamp('2005-06-01 00:00:00')] \n",
       "Training index type: DatetimeIndex \n",
       "Training index frequency: MS \n",
       "Estimator parameters: \n",
       "    {'concentrate_scale': False, 'dates': None, 'disp': False,\n",
       "    'enforce_invertibility': True, 'enforce_stationarity': True, 'freq': None,\n",
       "    'hamilton_representation': False, 'maxiter': 200, 'measurement_error':\n",
       "    False, 'method': 'lbfgs', 'missing': 'none', 'mle_regression': True,\n",
       "    'order': (12, 1, 1), 'seasonal_order': (0, 0, 0, 0), 'simple_differencing':\n",
       "    False, 'sm_fit_kwargs': {}, 'sm_init_kwargs': {}, 'sm_predict_kwargs': {},\n",
       "    'start_params': None, 'time_varying_regression': False, 'trend': None,\n",
       "    'trend_offset': 1, 'use_exact_diffuse': False, 'validate_specification':\n",
       "    True} \n",
       "fit_kwargs: {} \n",
       "Creation date: 2025-11-26 15:05:55 \n",
       "Last fit date: 2025-11-26 15:05:57 \n",
       "Index seen by the forecaster: DatetimeIndex(['1992-04-01', '1992-05-01', '1992-06-01', '1992-07-01',\n",
       "               '1992-08-01', '1992-09-01', '1992-10-01', '1992-11-01',\n",
       "               '1992-12-01', '1993-01-01',\n",
       "               ...\n",
       "               '2004-09-01', '2004-10-01', '2004-11-01', '2004-12-01',\n",
       "               '2005-01-01', '2005-02-01', '2005-03-01', '2005-04-01',\n",
       "               '2005-05-01', '2005-06-01'],\n",
       "              dtype='datetime64[ns]', name='datetime', length=159, freq='MS') \n",
       "Skforecast version: 0.19.0 \n",
       "Python version: 3.12.11 \n",
       "Forecaster id: None "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and fit ForecasterStats\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterStats(\n",
    "                 estimator=Sarimax(order=(12, 1, 1), seasonal_order=(0, 0, 0, 0), maxiter=200),\n",
    "             )\n",
    "forecaster.fit(y=data_train['y'], suppress_warnings=True)\n",
    "forecaster"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f855fc34",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2c8f34a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2005-07-01    0.957860\n",
       "2005-08-01    0.960092\n",
       "2005-09-01    1.108625\n",
       "Freq: MS, Name: pred, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict(steps=36)\n",
    "predictions.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fd4a82ae-5258-49bb-9f6d-380b3cb67563",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-06T18:53:35.080031Z",
     "start_time": "2022-03-06T18:53:35.056284Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "data_train['y'].plot(ax=ax, label='train')\n",
    "data_test['y'].plot(ax=ax, label='test')\n",
    "predictions.plot(ax=ax, label='predictions')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3e220454",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test error (mse): 0.07125639765150053\n"
     ]
    }
   ],
   "source": [
    "# Prediction error\n",
    "# ==============================================================================\n",
    "error_mse = mean_absolute_error(\n",
    "                y_true = data_test['y'],\n",
    "                y_pred = predictions\n",
    "            )\n",
    "\n",
    "print(f\"Test error (mse): {error_mse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a544fef",
   "metadata": {},
   "source": [
    "## Interval prediction\n",
    "\n",
    "Either `alpha` or `interval` can be used to indicate the confidence of the estimated prediction interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b43fa507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>lower_bound</th>\n",
       "      <th>upper_bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-07-01</th>\n",
       "      <td>0.957860</td>\n",
       "      <td>0.857820</td>\n",
       "      <td>1.057900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-08-01</th>\n",
       "      <td>0.960092</td>\n",
       "      <td>0.853863</td>\n",
       "      <td>1.066320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-09-01</th>\n",
       "      <td>1.108625</td>\n",
       "      <td>0.998232</td>\n",
       "      <td>1.219018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                pred  lower_bound  upper_bound\n",
       "2005-07-01  0.957860     0.857820     1.057900\n",
       "2005-08-01  0.960092     0.853863     1.066320\n",
       "2005-09-01  1.108625     0.998232     1.219018"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict intervals\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict_interval(steps=36, alpha=0.05)\n",
    "predictions.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c7c23b",
   "metadata": {},
   "source": [
    "## Exogenous variables\n",
    "\n",
    "The addition of exogenous variables is done using the `exog` argument. The only requirement for including an exogenous variable is the need to know the value of the variable also during the forecast period."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada5e28a",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,191,191,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00bfa5; border-color: #00bfa5; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00bfa5;\"></i>\n",
    "    <b style=\"color: #00bfa5;\">&#128161 Tip</b>\n",
    "</p>\n",
    "\n",
    "To learn more about exogenous variables and how to correctly manage them with skforecast visit: <a href=\"../user_guides/exogenous-variables.html\">Exogenous variables (features) user guide</a>.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "06add010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2005-07-01    0.904512\n",
       "2005-08-01    0.932239\n",
       "2005-09-01    1.089341\n",
       "Freq: MS, Name: pred, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and fit ForecasterStats with exogenous variables\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterStats(\n",
    "                 estimator=Sarimax(order=(12, 1, 1), seasonal_order=(0, 0, 0, 0), maxiter=200),\n",
    "             )\n",
    "\n",
    "forecaster.fit(\n",
    "    y                 = data_train['y'], \n",
    "    exog              = data_train[['exog_1', 'exog_2']],\n",
    "    suppress_warnings = True\n",
    ")\n",
    "\n",
    "# Predict with exog\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict(\n",
    "                  steps = 36,\n",
    "                  exog  = data_test[['exog_1', 'exog_2']]\n",
    "              )\n",
    "predictions.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9745bd49",
   "metadata": {},
   "source": [
    "## Using an already trained ARIMA\n",
    "\n",
    "Forecasting with an ARIMA model becomes challenging when the forecast horizon data does not immediately follow the last observed value during the training phase. This complexity is due to the moving average (MA) component, which relies on past forecast errors as predictors. Thus, to predict at time 't', the error of the 't-1' prediction becomes a necessity. In situations where this prediction isn't available, the corresponding error remains unavailable.\n",
    "\n",
    "For this reason, in most cases, ARIMA models are retrained each time predictions need to be made. Despite considerable efforts and advances to speed up the training process for these models, it is not always feasible to retrain the model between predictions, either due to time constraints or insufficient computational resources for repeated access to historical data. An intermediate approach is to feed the model with data from the last training observation to the start of the prediction phase. This technique enables the estimation of intermediate predictions and, as a result, the necessary errors.\n",
    "\n",
    "For example, imagine a situation where a model was trained 20 days ago with daily data from the past three years. When generating new predictions, only the 20 most recent values would be needed, rather than the complete historical dataset (365 * 3 + 20).\n",
    "\n",
    "Integrating new data into the model can be complex, but the <code>ForecasterStats</code> class simplifies this considerably by automating the process through the `last_window` argument in its `predict` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5e37dec8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dates       : 1992-04-01 00:00:00 --- 2005-06-01 00:00:00  (n=159)\n",
      "Last window dates : 2005-07-01 00:00:00 --- 2007-06-01 00:00:00  (n=24)\n",
      "Test dates        : 2007-07-01 00:00:00 --- 2008-06-01 00:00:00  (n=12)\n"
     ]
    },
    {
     "data": {
      "image/png": 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LFFdqCv7y6Iv4xelX4eJrSHSV4oG7rvb/nMTS6OGDcOFv78TBJ1+Bm+5+VHYGRXiZOP2yWzDlsPMw5/fhxeP8Rcux74xJgSuVaePR2NQi3xL7UH7i7j3YvG1nxHX+/ldn4r2PvsFhp/8Wn32zEA/fcy3y85QPwfKyfnjy7zfj4y9/wBFn/A4vvfURbr7qgqDfnzRuBB7/yw14Z95XOOzU3+Dvj72MG644F6cff5h/jfR3FhXmKWuaPhE1tQ3YT113amoKpk8Zi/kLl8W5pRmGMQvPwaej8virIFnkWtmC3EJgwHCAXMXicuV7GZmA06lxHneb5zzGErImLHQezyuRZNdvcTNw0HIntnYGVJlbMk+oaZ3H7BRgfFYE57HdYZobqqUoBxg1QHnRHzd01SMddu3zaEca9050XFFsv586P7Grslfe+cR/f+uO3fjDn5/AvJf+IbuCrW3tcsP15as3YtmqDfKYqG2VypUqIWaC1zU0obomcpsHElxnnXQ4nE4nxo4cDLfbg3c/+loWZl98t1i+/X5R9x8o1Ivz7Xlfyffvfeh5zDn7eEydMAqffLMQ559+NLZs3yU3fyc2bNmBcSOH4jcXn+r/fXIZv/lhKR7491z5641bK2WxePkFJ+PVdz/F6vVbUN/QjH2nT8T7n3yH/WZMxOMvvI1LzlZaQU2bOFoWkAt/Xh3nlmYYxix8Uw5CR3oWUvtVAJUb0ScpGxK4n99Pac0jQtZU7Uw5hfXV8pe+jBxIaZmKoLSLeFSdRysuACrS1ALTGgdqPMGKzMyCmWJXsCCj0PWyVvHCkj/ncYMqHtNMFo+zVNdx9Q4J4UZXc8FML4YcuecevBU/fvi0HLp+8yml6bporv7cax/ihCMPxAcv/QO3XH0hZkwZG/NrLPhpJXKyMjFx7HDsO10RitSjkyqihcv3XQ+O3qp1m/33KR+SnMviIiW8PmrYIPy0bE3Q4xctDRZ59Jgfl6wM+t6PS1Zh2OABsqglvl+8XBayVKE9avhgPPvq+0hLc2Hk0IHyGn9esY5zMRnG7lBoND3LFpW6ltJ/aOB+fknXfEchIkVLnEIDxxRm5wOlgwHJB2xeEZvzaME+rFDb4lCuYyhWha2JmZqimZJUIEvdNBstynn0h6zXhze9zBKPvcJ5zFsQvwam3eBKSZEdPjNqpjIz0vHyo3fii/mLceXNf5PD0SQaX/7XnUhzKbvj828XYdYxF+OIA2dhv1mTMffxP+G5uR/gzn8oLl80kNBbuXazLMymTx6Lr77/SRaQ//rzDRg+uBwjhlTg+4XdX416PMETBiRIftGnFyRoaQrR3tPGY/nqDWhuacOCxStkkUuOJIW2GYaxOTmBnG0rK3Vt5TwWRBCPdN6pr4ZE4o4eY5RLKxqD79ocvbspch4tcB7LVeexUhOuFphZMCPE49o2YHRmcNGMmCyzoxNosWhk4kxVPC5YH16xsPMYA60+h6n/EmHksIFyjt89Dz6HH35aifWbtwcVywhIVL7x389x1S334/a/PikLLILCz0Q0Io6E134zJ2GfvSbIIq2+sRnrN23DVZeejl1VNXIYOV7WbdqGqRPVKj6VvSaN6fKYmVOVHEvBzKnjsHFLJXw+5Z03f9EyOZR97OGz5TUS5IgesPdU+bHiewzD2BgSQgJnr/Ak9AlbRxSPSiqSVGCg81hYFhCP0eLPeUyxUDx2/ZmZTcJFzuO8eoe/56SodRiiadPjsaDPY1Y6MGmQcB5ZPPYpduyqRkenGxefdSwGV5ThiINm4ZrLzgh6zPWXn4MjDt4bQwb2x+gRg3H4gTNlIUbsqa1HW1sHDtl/OvoVFSA3JySbNyTv8eB994LH65VFqhBmJx99cI/5jj3xwmvzMGxwOW695iLZxTzp6IP8hTCCx59/G7NnTcbVl54hu52nHXcoLjrjWDz2/Fv+x5A7SqKWfl+E0WndVHmd5nLJApthGJuTXRC431cLZkgkCsGodR61ldaCFqUAUhLC0khBT1NtosQh+jyafAGQ7ZSQr76k5WFrNedxeSuwvUNxO6epcy+GqJXWmzsc6FSdR6eDhJQ5vR6nD3MgNcWBbTUSKuvCP4bHE/ZSyFGk+d/ktH3x5qNygYkoOhF0ut246bfnY97cB+V8SK/Ph8tv/Kv8M6/Xh1v/8gTOO+Uo/PTxs3jmgT9EfC0K/1IHeq1QJCePilC+S9DRIxF86bX34shD9sHHrz4kt9e57+Hngx6zbPUG/OqGv+CEow7EZ288LIviv/7rRblYRgsJREmS/EKRBGVTSyt+Xrme8x0ZJunC1ql923V0d3af80h43cZvq2yli4XIr4wKnzXOoyiWafQAzWGie24TQ8TCeaSinR/VXSZC10PUkYlb2gOClkhzmlssE8l1JDjnsRdx6pybg76mCmZRxSwon6pUGBMPPvkqHnry1Yi5mNQWh/71BDl6A/c6Ieh78z7/Pui1BHsfEzw+Mdxjxh9wlrwmwSdf/yj/0zL3nWBh+MGn38n/uoPaFWkhITnhoLO7/R2GYWwEzU4W9NWCGSEeNy8HRu0FpKUDmbma6TItXR2+FJfx+0R1OaPCIufRH7JWNbWVzqMQj3Ue4MdmB04qluRJMwflSTi/RFnI2hDxSOtqN7VYpmfxaHSTcBaPDMMwjG45j322z2N/VTzuWK9UXecWKaHrcM6jx4TClCzhPEZu6dYFv6g1WzxKEfMdA30eJaQ6zSuYqXEr4pFe9/ACCccVSch0Ap81AK/WOJRqWxgramlY0ewxypPXtUjYa7goloncXpCdR4ZhGMZeuNKA829TKoQ/fDq885jSx51H6u1Yv0cRj5QD6RePTV3D1qmpxgv6WJxHNWwtOa0JW+8IU2lNCJfP6J6KDkgo9IetgW2dgE+C/3vv1QJnrnWiQ3IgLcUh/4xyHo0Sj+fOduK+c4L3RW2zhHWRZ3uY1iSccx4ZhmGY6BgwQukdOHE/tdFZuGrrFHsIOSNDwqG40oGi/sr9XVuABqUReETnUXX4JKPWmJahhM1jzXm0qNp6gCoed0ZwHs2qbC5IBVIcAfHY6HVgqdqI++U9DpymCkezwunnH6RItO01EnY3SGjrlPD8l90PNWHnkWEYhrEXxf0DTcHJbWyus1/BzJBxwNk3AeuXAK/db85rkqB2OIGmWqC1EWjYEyia6UY8GratRLEMNSR3d9g+bN1dg3Azcx5FyLrJGxiJeO46J6ZmS3h1jwM+7QUTADccSIdkiCM6ZYgD4wc60O6WcMTdHjSEmSbTvXh0yGFvyaBCcHYeGYZhmOgoGtC1FQ2dULNtJB7LRyq3I6cCg4J7zxqe77h7q3KrjiBEQT9rqq2z8mN3HbVNwg10jwtSqABFClsw01PY2ug+j9p8R8HqNgde2ePsIhyNdkTP2l950g9+kqIWjlrxSKQZaCCzeGQYhmGiQ4RmteIxKydYbFjdJNwvagEceIo5HfhEvqNoyC3C1iS2KYQc2iTcXzBjUNg6J458R7kKXIStjduHT4/04btJPuyXK3URj5HC1mZNmPGLR40A6w6PEI86K6nMNODEmcqTvvxN92HqbsWjgW9FFo8MwzBM/OJR2yDcDgUz2qktg8dCEmP6zCqWCXIe1W1EE7Xa28wrmMlO0Hk0UDzOUI3YA1TxSEUq5aqGtjpsXZiqvFBtlOLRrYa29V7XsXs5kJvpwOZqCfPXSfGLRwPTflk8MgzDMD1D7qLW1RMiTZvvKBeBWC0e1TVuXyffeA842Vj3kbZLycBg57GpThGMgnZyHSXzcx5jFY8GF8zQJBnhMk5Vp7b0S1WcO6pc3uW2tmAm4DxG90Ieg6rAz9xfkWavfOuLK2fR3+uRnUeGYRjGclGmDU+LUXzaNj1Wh61pfXnFyv15z8oFI9KA4WgdMtG418wvVkRgZwfQWBNoeSPuh+Y7Eh63sdXW8bTp0VaBG5TzOFKN4BNTsqWgNj1VbhJjPeU8wlCKXbGFrd1qHqSe6xpRBuwzygmvT8Jr38cWsvavy4SKaxaPDMMwTPQha+GaCedRWyxDWOk8kpBzOpUK4+ptwI/KJK6GKYcY95qiIIaqrLWIvEeiLTBdxhznMT/2BuFB4wlTDRePdD/HKWl6PJofHu5uukw0eAwQtSLX8bPlEnbFuPsEnV6H4VNmWDyaxOtP3oM/Xh88AjAR/nHn1Xj6H7fo9nwMwzBRicdta5Tb3AKl4MNOzqMQtCLncN1i+cYrqo+NgEYQhnMXRbuecD8zLecxPufRqH04KjMQg6Xm2pOyqMdj99NlLCmYiRA+D8VjgKgdXqY82Xdr4k+26DShUTiLR4ZhGCZ68Ujj96h/IPU1JKdPCBUhkKxs1SPyHeurgkSaoSMTw7XikdegdR6Df2Z4tXXcBTPG5jxqnUcRuhbOY2WENj3m9nmUYgtbS/qvq0zddbvq4xePbnU3phuotrlJuAmQS7jfjEnyv0vPOUH+3qxjLkF2ZgZuveZi7L3XeLS2tePL+Utwx9+eRG29crV49GH74qpLz8DQQQPQ1t6BFas34sKr/4QrLjwZZxx/mPyYyiXvybenzLkJ8xcut/CvZBimT4jHmp2KOKPG2CTWVKHiaKyBRELKyrB1qPOoFoAYWsTjdx414we7hK1DnUd7Fsw4jHYeMxRBtL5dEZJTsgMOVjTOo+Fha1XL10ZZMOMWOY862nD9C5Tn3BWj7tfiltcvGeo89grxSD2R4oU2sSsFSPUG1cJ1S1s3B3k4bvvLExgxpByr12/BXx99Uf6ex+PFB//5O1566yNZMGakp+GWqy/EY3+5Aadf9geU9ivEQ/dci7sfeBYffDYfOVmZ2HuvCXA4HPjXc29h1LBByMnOwjW3PyA/X31DyIcTwzCMEQ3Ca0k87lHFY2kgbN1Yq7SscdrQeXRa4TxGEbY2QjxSX0kalxhXtbXHFOfxjRoHbqyQ5Mkte9w95zx6fNom4Q779Xl06Leu/urbaXcCzmOn6jyyeOyB9Q/pYf1Hf+lQ8esoEyJUmppb0en2yO5hdY2SAfu7Oadj+eqNuO+fL/gf9/vbH8Sij57F8MHlyM7KhCs1FR98+h2271SuYEl8Cto6OpGW5vI/H8MwjGGQIMktVO7X7g6Is/wQ55Hu2Mp5FDOkDTzVUZN0ojVO51HvGXIiZN3RBrg7bVMwk5sioSwtWDxOzAI2tivf2+mOLL46DWrGHalgJvqcR+jqiOZlkhmmPNnuhJ1HY/s89grxmIyMHzMM+82chHXfvdrlZ0MGDcBX83/CNwt+xqevPYwv5i/Gl/N/wvsff4uGppCqPYZh9INalIzbG9i6SunVxygUlgWcrI7WgDAqqQAysuS7jqYa44VarOLRn1uYalyvx0jOY1O9IhJpe0QSj/LaXIAnRpEXjXgMrf6Opc+jAa16Rqmu4+5O4KcWoNkL5KQAE7KiqbZWbvVO4StJldDgJXHqQJpDktcTm/Po0FU8lqmuY12LhPbYPKqwrXqM7PPYK8TjyKvcCYatU+D2es0ZY6WSnZmJj7/8EXc/+GyXn+2uroXP58O5V9yOKZNG46B9p+HiM4/F//3mPPzy3OuwrXK3iStlmD7E6OnA8b8GVv8AvPWw1auxX75j7a5gcSbmSFNrnNZmw+cid0tGtl/I+tcnJqYQJOKEODJDPNIZhbYXNRBvrgvb59FfcW2EeIw1ZG2wUztSk+8owYGlLcB+amqmFTmPQ9IlrJ7qw6cNwLGrU/wha3ITSVBGQyf0XZfId9ydYECRw9YG5SBqoV3lSVGqk4wUj263BynUf0xl2eoN+OVh+8lC0Ovt2ghUHIsLl6zCj0tW4f7HX8EPHz6Fow/dB0/85x243e6g52MYRgf6VQSPm7MhUnE5qg48GdJncwNizirxmKmOCWluMGW0XVSuY3N9QIwJ59G/rg7zWvWIRuXlI4AdG8KHh+V1uYwplqF9Eis+453Hde3K2W1JqwP75Sln3XZf9yMBRZ9HPSe5TMxUwuC/KACynJK/QbiyjmgnzDiUdel0Ku6v6v7dDYmpEX/Ymlv1JD8kEqdNGoOB5aUoKsjDs3PfR0F+Dh6973pMmTAKQwb2lx3Gf/zxd3A6nZg2cTSuuOhUTB4/EhX9S3DMYfuiuDAf6zZtV5+vCuNGDcWIIRXy86WmWjwSjGF6A9R6RhReGNVGJUE8vzgXLaOmwztxf/NeNJJ4FLTUByp1rcp5LFAn3oh8zC7hYYPOpBGdRxqRuBb44cPg0YSqNHEI91HvdSUUtjbuAmBkJvzOI/GzJgNLCVk7TG3GXeyS/KHwvXMCxTLRzrU2Yl1l+YnnOwaFrQ0sT2fxaBKPPfeW7DB++cajWP7Fi3C5UnHChTfI7uHL/7oTn732MO68/lI5p5FC1k0trXILnxcevh1fv/MYbrzyXNz596fw+beL5Od78c3/YcOWHfjwpfvl55s5dZzVfyLDJD9i5B71MCxS8/zsRHE5pCHqe92VZuLrDgi06SHI2SOHT0Aulz9fzmLnMUjYSsa3xcnKDV8w0xPCqU3V23lMPGxtxLYSbXrWtanOY0tA2OzsIXpoRNiaZmoLZudJ/mKZ2hiy4DwG5Twm0uOR6FCdR8557AVs3FqJ4y+4vsv351x7b9jHr9+0HRf89s6IuZi1dY046/LbDFgpw/Rh8lTxSBSXK21p7MRehwbumynSCkOcRyHSRJseEioGN5iOuU2PgEQtiSGj2uKI5w3nPHYDObWSkc5jHGFrh4Fh6xEZwc7jijbFuSPnb0c3DcKNKpgRYWpi/1zJL2CjLZYh3AblPMY7ljC0STiHrRmGYYyGWqbkFXV12+wC9e6bODvwtVkiLStPKUSRfMHCTNuKhlxIgxtMd4HE/X7HAelZ3TiPBvdUFCFraokTY9FLIMxvUM5jQs6jvsdWfoqEEleweGz3ObC6redimWDnUb/KBBGmJvbJBcrU9dVE2SDciJnb+uU8KrcsHhmGYYwmpzBYYJA4sRMT9g1UE5tZmCLyHanptTaHUCvSSKiY0VNRu2/OvQU46DTg6IuDnce6MM6jUeuKNF0mJvFoo5xHg/o8imIZcvdafAGltaBJub9OFZSmhq3VnEeCWvQcki/FnfOYplvYWh/nsdOEghkOWzMMw2jzHe3qPO71C+W2YY+yVrMKekQREb2ulvpIzqPBjmheMXDmDYFcw3GzgJXTA/vPCucxxpA14RA5otSqx5CwdRwKJGgfKiPu9GBkZqBNj5Y/bHXgq0bgjdru1ZchBTPqZu/0KdXSs/NiaxAelPPo1CfwUSqcxwRzHv2zrQ38iGDnkWEYhhDio7HGP46P+tHZgoqRQNlgOTyasuxrc8PWwl0LdbK0IeygnEcjp7nkKsKR0gv27AAWf6Z8/5g5iuAJLeQhjFyXf7pMHOLR39pIxzN8emagkKolgSbhOh9foW16BNUeB17c45RD2GY7j0I8ft4YnE8ZU86jFPy7Ca0nh3pOO+DzSaiOY9fZPmxN85Wfe/BWLP7oWVQueQ9HHbJPj7+z74yJ+N/LD2DTD2/i23cfx+nHHxbvehmGYYwVj1tWKQ5MWnpgJJ/VTDlYuV35vT+XzbRJLpGqiYOcR021tZGi9ogLFEeYXNBX/gJ8+pISphb9JuV50iFtcYxqiZOw82hA2Fo7mjCexuOhTdV1nmm9Xs1xjBUjCmb6qZr9nRDXM5acRw/0E7Wi0npPU2CWd+Jhaxu16snKzMCKtZtw872PRfX4QeVleOGft+PbH5fi8DOuwpMvvou/3fZbuadhPNjEB2BsAh8PjO7isW638k9uyG2T0HX5cOV2zY/mF6ZkRcjra6oFqrYCVduUPo8+E8LWA9WJNv99QhkfSQLpf5opXaGV1kY7j3rkPOrZqoeKm+ItljGwL6a/TU+I82hVYYoDkt95/KjeITcpF8SS8+jWcV398/XJdzSr2jrmp6Y+g6LXYDScf9pR2LpjN+68/2l/C5pZ08bjsnNPkOc1x4pPkpCdmYGWth4ybJleDx0HdDwwjK7ikVwt6mfYr0Ke5oL6Hdaui8SYKFohoSbcLjOrrcM5j/Tee/pW5RKO7vudR4POWBSOzS0KbAfBpuXA8m8BapperQxRCKK3O48Uqu4/FNi6BsgRPR7ji3s6aD9SVT31OdXxImCY6jxuiPO07VbFnV7iMT8FSHEEKr1/bAYOEDmPFhXM9C/Qp9JaO2Emw8CcR8MvXadPHouvFywJ+t4X8xfjj9ddGvF3UhyOiKP33vvft/jtRSfh4Wfe0kVAOihLVdzaSIjYcV12WhMJx99cdBJee+8LeTY5IW7tgh3XZcc12WVdnap4TG2ug692J+h85SDxuMHadfn6VcBDAqOjFa7WBjglL0imOVJdpqzLnZUrB4JT25vhjPh6KUiBpPS9S0lFmgHr8hUPUMKEbc1Ic7cHiWfpf8/Ct2k5nBt/hiPktT0+pVduiiutm/XHhyc7Tz5OUjpakRLDc9N+k1SnltYVy+92WcNRF8I3fl/5gsdRvV3+Wx2tDXEdG/Q7cm/hVCdcrrQu2zIeqL1OiUu5sKj2piBNqLZYkPWAWy5M0ePYGpBOW8mHRlqWMxXfN3twQJ6iUJt90a3RRdtKdR7TnY6E11VeqDxXdWNiz0Xr6lAFcIYrvufqjGIGvOHisaRfIaprgn1Y+jovNxsZ6Wlo7+ial5GbkYHCLE1LCg2+1k589eUiXHXhSXJ5kmToRGrGjjjUYPUXXy6Uj4fSXCV0JG7thtXr6iwoRevgCchb8TWcqtth9ZoiYdW6qDBmsyoe+0udaG9vBGXPpZZWWL69moeMBmUXptftRnlBAVrT0kBBdTq509dGsz2nQBaF/ZwSMrt5PW9GBrbSnZRUeV16p5S0DBwOCkqnN9WE/7t3rgYy05V/2m87ALIZ8rJzkKPz9tqZUyA/d5HDG/NzV6lztwty85CXwLp2lAyEfBYtHuBPs8jxdKBfnM+5WQ7zu1BWWARXSoLJd/R+SqXnq0GnREZtASriODLy5XW0y85jRQG5q4kdXWMz6IiuQ4PXiYqCAqz20cxzJdSfTmtURWFPeNAq3+alu9R1xc/wUkoIdaOtIwMVBcHHcKy41XzX3IxUVBSo+cAxsKlGLRpMtlY9Te3taO2MnOy7o74eC1eHDJpPQKXTiaGqqUm+4rILdlyXHddE8Lq6x33IeZBG7YXG3duRsX6xLdZkt20lUaEBuXs+L3bv2AJJvXLvyFN6B1q5vTyZStGOe/cW+bPPUaSEj91wyF8bjVttwr2nehec3bxealZg++xobApMK9EJr0sJEburK2P6u70dSoSqwe1Bg87by02N20mG1FTF9NyyK6g6j/WdbjQlsK7OdEUcOFd8B9+oaUBaJlp3b4vr2KB10X4jS2Z3cwscOmyvAdmKAK3qBLbXx5eL6aZeOiq7G+r9LXLiZXKBWJMkb6f/NkqoLgOq3Q5sqotujS5yHvMVCeVxdyb8XszOkCeeY92uNuyoj7OyyO8eK3kCEjyGfUYYLh6r99ShpDj4Coi+bmxqCes6El5JgtfkD2o6MURj1ZqNHddlxzURvK7uc9a8WXl+AWT5miJg2bpELl1jLdyUI6fmzkk5BfCmZcDtrbdue6lukq9qm7yGVNVVkJwpxq+JUlTUSmZPE/VyjPx6kpvcGwU5fK332tQm4JRSEMvfLaqtvQ6n/ucVNefR09wY89+bpkYBvI6UxNalFjT5vngN+PhFYNBoeDcsjfs5heh3077XYXsVpyjRwZ3u6MKh4WgRyYV0nPm86OyhtU9P5DsV8VjtluQ11XiBMT855RzGzhguejySIqFSHcrzJEKZKkR31PnQ6U0sotqpXvy6UhJfl2V9HhctXY3Zs6YEfe/AfabJ32cYxgQooZ7IiD180WegxtPaHo+d7Uo1MZ1E89Wxd1ZRMlC5FcUgYpKLnlW63YkjKp6IpigkqEegAb6EKBpSK+Et7afYpWAmkWrrBLYVucLiOKAiGVrH2kWBxujxoHMLof7qJBcxOzqRVj16Fc30U/+0PZq2PI1eB1pjFKVuHautRaueRBuEawtmbNeqZ8KYYfI/YlBFmXy/or9yVXjTb8/Hg3dd43/886/Nw5CB/fGHqy/EyKEDccHpx+C4w2fjif+8o+ffwTBMj+IxfB6xZQwaAwwcDVuQX9J1isqenfKNu7DMokWRsEgDClXxWr0jWKQZPcklqBVNs1KF2x1ax8aItYntUBubeISaW6h7dTrtGzVsnVi1tSvxOdbtrYkJRu26xH7UaR8OUHuW73I7dBGPelQ2F/vnWCf2PB6dmpenOoGSPLVVT5xdlsK26rFTtfWUCSPxxpP3+r/+43Vz5Nu5736Ka257AKUlRagYoH4QA9hWuRvn/faP8uMuOft47Ny9B9fd+c+42vQwDBMHaTZ0HsktOeM65f4DVwKSxSH0cCP4aiuBYRPgLrBQPPYrV5w/mu4iJrwYNRM5lgbhYXCItdG69F6btk1P3a7Yftco51G4jvQ3k1NtRasefxulBEeShBOPejmPQjx2JlbQRpFcKoLWw+UTPR5jGUVoZJPwUrXWptMjoTb265CIYWtb9Xmcv3A5yqceF/HnJCDD/c4RZ14d++oYJpnIKQCOuQRY/CmwPrg9lWVQ3pIdw9Z00hOuTVEZUFNpnx6PAur1SFfxBaX2CVkbNZkk1h6PEZCLLYwQj8L9JYePXLY4nEdJb+cxgR6PQeH0RNIPshPr69jtzG0bha2Fy6eXeOynrmlPgs6jW6ewdf8C5QmqGvTpguf2qi2E7DSekGGYCFCl44gpwIwjYDvX0W5ha+FoaXPZ7CYeaxWHy52n/swK+lUEh6yDnLQUE53H6MSJw6jpN0I8qvvEFs5jDK5sOHS5CMhOcKJMOHSeFKRH2Fp0FyCo16NuOY+JrkmCLmsqy9evQbhtZ1szBibsn3UjMDK+sY2MDRBOBDmQdkG4jnZzHkUuHVE0wJ7iUXW4JMpts5Hz6M/hM2M8YawCyeczRtgWxlcsY2jOY6LOo57iUdewtU9n51G53eW2R34hUZQa+yjC8Gty6LKmsgL9RhMSHWrBTLqdch4ZgyDHaugEJel8PeeDJiUZQjwqffnsJx7t5Dyq28oOziMJJAqhU0GIqLYm1BYvplQ1R6KfKh73hHEezVhXjOIxUNms86mFUhviFY9G5zwmLB5dNst51G8f0hiPMvXP0yNsTegTtoYuYWuPeptw2Fp1HnfpUGltlvPI4tEupGUEfxgwyYdw9qgvHrlVVheBEGqDZ/s5jzld+hha7jo21wdXDIt+ika0eInELy9VinfeeDC4kGePxnkUokOEFnVuxh1WPEbZikbvYouuYevYxaPD7zym2qZNT3CrHrvmPKbo4vCJ/t67E3YeSaFJCQs1ErSiYGaPLmuCbjmPu3XKPgiIRweMmibM4tFuDpEIQzDJh9pMWSa3INjFsoPz6EpXihnsFrYu7G/tkNFwIWut82jWNiMRMfkA5T4VXv0wT7nfWBtcJBLUT9Fg8ZgZY8GMUW2EhHiMtdKaMMoN9QvrOMtjxX5LpM+jMBt0FI/+demQFiHyHUmkieKSeBE6L1Ghlp+iFN7o0aqnUyc39MVvfFiyRcLCDYmPg5TXpRbMEGkpFMaG7tjkTML4K0/lDwPjGnsyBqJ19ih0bTfxKH+dZb+CGRLd2q/NRhTENITsL03Y2hRxq82VHTsr0JpG6zqGOo8kiNwJxgOjSS+IOufRAOeRPhtzC3XIeTTIeWy1MGxtQMFMwD1OsU2+o54unwhZN3oSF7Qe9fcT7T354wZJ/qcXwnkUoWsjxCMXzNgtbE0fcHbKTWPiC8XapWgmVDza5djSbisSaKIgwgoGKAMPurQL0jZdNiO/MFs9ZkQz7oqRXYtlxM/FY4wOqcdaba2j8OjiOpKAjbVNj2Y/6u4gi2O43cKCGZvnPIo2PYn0eNQ75zEwXUbHNTlhK0STcCMbhdvsT+7DCPGozWNhosLXfxhahky0n/Not1Y9dAK1S96jNmxN67KyaGbIeOV2y6qwzqOMGXmP4oJjxwZg1Q+B72vb9KhxCb178fUoTqIMzRrSqscfso7DdSSM2lba6TtWOI/0e+JiUNewtU+31APRIHxngi1x9HQe/Q3C9RCP0K+IR18c6FD7CBnV65HFoy3FI+c9Rs3MI+E59w+oOvISSCLMZ5ecRzsQGqa2jXjMCXL7LBOP1EeR3m/uDqByQ/DPyEXz56WZKB6b64APn1KalNPr71jX9bFGOHzhXGshuKx0HosS6PGodZANC1snWjAT57rEeYIucjricGR7WpcO22uA+rbZrYPzqFfOY7HqhiY6XUbv2dZ6Y/SUGc55tKN45IrrnqGr4iMvAKYe7P+WRB/m9dXWrIeqq7X9AEWOls3C1pJdwtYil277eqC43DrxOFR1HbetDV94Qidmyjg3RTyqEYfmBqCjDXj2dkVQhhFNdIKXjO71KJw1Gr2ndWHNznlMpMejkS6tXhNm4l2XAT0e9a6YDziP+oWIaQ60PmHrxBWfW8f2QckmHtl5tFvBDMFh6545+SpFOFKIRZzYrOzHF+ro2SVsbdeCGSFMtq+VbyQRmrQsZL0y/M/9x1aaic5jfUC0RXDbDOunmOAEFb/w0DVsXZpg2NoA55GeS7y3Em3VE2/Y2ohK66B9mGKb0YTBYWtJl4IZPZxHj7omqt52WtszoguiSMaoRuEsHu0Ch61jO8nSKEASjq/fD4c4qVgpHrUhazsWzIg8JjuErelYF/tqx3rltrAMksPkjyNqgDZ4bPfiURRbmBq27nnMhCk5jzH2eAwWRDqErWmm+DFzgIGj4u7xKGPELHDhOtL7qr3NorB1viHOo545osJ53O22j8tXZEDOox3dR+E8pqcaszAOW9sFDltHT56a29hUB2xYChx4ivK1mc2cQxGijBpLk0uVU2iP61AhHptq5X6GtghbixMvtZihvD66daXBQ25tXa156ygbquy39hZg15bunccUE6utW+qta8YdtpK3yfzRdoedDcw4POB+rV0M7I6wj3pCFWm6VFsPGqN8/pCw9VdaS73MedQ/51EP51HMtk60LU4/1Q3Vo9rarWn1Q+KxwxYf+gqc89hX4Grr6MlVp240qX357BC2FoJoz06g/xAgLb1ryNjKamtqgE3NsNNt4Dz6q1RJlEhK4+fSwXDTCXlbSNGKkQwZp9xuXRNofROCw+NWpIHNnMdA42sDC2biKQgR60okbE2vO+so5f6Gn4Fv3wk41FY6jyOnAaddE/y9aPaVUYUpBvR41DNsneWUkJeqf86jbq16dKkAhx+5XY8+Pb51oVPd5iweezskNgQcto7OeWyss88MYuE8UqVsWz8ljE1Omle/Ksj41qU6jWrDcls4j/7G02qhQY0qHvNLrCmWiRSyNvPChE7U4n1vZdiahJsoAImjh6Aujqi2PdCrf0fC6CUeywYHmsmTC+puB37+Ku6nS3g8oc0LZkSD8FYv0KTDECS9WvXoGbb2kl6UAKfDvmFro/o8sni0A640QJvvxWHr7skLcR79CfE2cB7phEcCMjMbEjlJDRaLR+F+itF7dhCPQc5joAWL7DyaBYm1gWPsIx6FECBBEcXEksAJXsd1zTgCOPxc4O1HgFULEiuYScQRTbCKuQt6THLRdlBY+hXwzVsJL8svHoVQ034dDVn6z7WW0enCRIwmVFxH/Vw+vSbM6CEeAYeci5nuSDycblzOozHPzwUzdsClCVkTHLbuHtHPkWb+aseP2cF5pPw5Eo/k8tmhaEYbtiZsEbYOEQe1O+UbU53H8hGK208n3pAm3JYUzIhjRRYCkrmFKYJxs5TbqYcot/GIRz2Eh87iUbdtJT53KH9YD0SIP97tlR3b9B+zw9ZlYjShTtMzRc5jItNcHJD8TcJp3rYu65KUW9s6jyweezHpqngUH3Jp6ZC0rXuYCGFr5UPcIZxHO4hHOuE11dujXQ85LWKbqOLRHmHrCM5jvknOIwm16b/QTJXpRqxRAZQZrnYMxTIyehfM0PP0V8c0Dh6jCLg4xGMg5zFFh+PDbs6jvuLRL7TjXZswGYzKeUxQbA9IU0cT6iTS9HAeC1KVtjr6OY/os+KRw9Z2ch7pw5LCjCQc5dC1Tu+6XlttHVIwY2nY2obOo7go0eQ82qJVj78QozlIPHpzCuCkY9+oPFHqF3jwGcDovTRVvAu7/RXTCmZiKZYxYgxg/6GBv5G2zcipXdMLzKq2TnBySxf0ch5DLloThVwweW20rWJt10NtpsT+0bvaWqccUZHzuLPTYRvxKFzHRk9wpXTvFo+OuDsCdAeLRztVWotJDgUlkCgHqlU94TMB6MQmTrT+sLUdCmZEqK3F/y3rxaPqMtK0ErGu9Ezz+ymGEipKSHDThVNmDiTKZ203SDyecCUwQHXXtq0BFn2i5PZ1h1lNwmMVj3o7jxVqL0XB6Bka5zGGghk9hFocorVbxJrouKfPj3CThHqC9r8QtXqFreW1uZV9GOuFL63F6dRXZAv0KphR3zJ6OY96jAIMTJeBbrhVXZZmsziuaBKewQUzfUA80kmeWoYUlCjOI4vH8CdZOgnQCUFccdshbK11HoXgsFw8qvmONPdWM/vWp20LZWW1tTYsSRdOdEI0Kl2DQnwkHOn99czt0fcMNOvY0o4mjALdJ8yIRtzLvwUm7g8MnxgQzFEU8PjRY8KMUQUzQtTGIx6F60if0fRPLyhfOy2O/ajtwRmhzZTVOY8D1H6KeuU8ir2YkHjUtVjG5s6jwa16bKaV+3ibHneHP39Fdh6ZyJXWsuso2Sds7S+YoWrrWps4j0I8tiknTPWk57N6RGG4sKTRVc3DJiq31Aw8lmbTbtXVTrFb2FqfvLQu4nHxZ0BdVUA4kvDSXHiYki8X2spJV/HoSqzSmgYT6Em87XoMynfU09Uu8zuPeoWtE3ceS0SDcHcfEI8e5ZbFY1/IeST3Rbhp3K6nh3zH2qC8NNs0CafwsL9gpgCSDi0qEq60Fk6JGg72Wd283B+WbA6zD9OMFY+blsX2e6Y5j7GJR10LZqhFEokR2ge7NgNrFwV+FmNIVBfhoXPY2kHOXKK5mHpXWic6d9ugHo969hAV02V0cx5VkZbItL0Rqk+zqUO/z2W3T5/+k0aFrVk89mbSu4pHicVjD216NCF9q8PWFN4RgozC1sINSEmFz8oCFVFZ3SnEo5L36E2z2HkMV8UrqpoN2YcOjXhcHtuvmtbnMbZqa72KGoJcRxKO9F5KRDzqUcijd9hajzC/YeIxznX5nUf9xWMg9SB+99gJCaWiYMZGOY+jMhUFuq6t7ziP6Zzz2Eecx1ZVeHDYOvqKR6udR20YmASaJCkCMjsfHiv3o3Ae24PFo6XOI+VbihOlVhwY2U+xdJBysqX3V4xj7kxxtaly1p/zaEHBjCiW2b5Oud2xzn/8xuz+6TJhJo4WQVG4aVJqAuvyRzz0DVs7PJ74qvnjKGaKek065NP64EDhD065aKZKr1Y96m0ifR5Hqqfa9e0O/QtmHLAVHLbuSzmPWueRxWP0DoCotrYq51EUy7SpwlFzkvGKKRBWF8xobi3NeRSuEuX3CrdR/trAsLVwHbeujn2KhxGuNrXFOe9WYO9jAmFacnkovBqlk6SreBw4MiAaCTqG1y6OS5wknPNIQlo45jo6j0jYeSzsGvGwNGxtYM6jTmHrFp8DG9qpIZE9WvXQSkap4nFtO3QXj6k2U1Od6gZTWvXoDzuPdkA4RHLBDIetoyuYqbGP8ygEkersaR0kj5XTgtLDh6194nizAr9jEiIMjAxbx5vvqFmXbhcmk2YDR12oiOSSgcDCjwP5jrRNoq0E1itsTccIrUPrPBLf/1dx2xZ9Gp/wiDdsTWkeopWU9v2UIAmH+Q0PW8fqPKrnhxad2/ToWG2tN4kWzFSkAVkplKMIbDZAPLocdMc+9iM3Ce9zzqN6JcniMeqwdWDCTKr1owkFtnAeNS2g7FIwE6EYgvahIc24SaQNGq3c3xhjvqOeFybkqP3iXGDG4YHv0X4YOi7QvzfaYhk9m4TTmEYSa3W7g13G+mrg1b/H/HQJO4/+46MlvpY6Pa0r3v3o/9yxS7V1nmHOYyD1wF7isTNIpMXOaPXjcGMH4NVR5LntnvPIYes+0iRcfIBTw2SbXflZDrkGwskLE7a2rFWPv8ejxk1ThYDXyvQDbZPwoJzHLBv0eAxxTIxyjweNUQRkQ41/hnZM6FUFTk23hXD8+i1gserojZquqbSOXpjoFrYWxTJa19HK8LDfxdcxZB0ktuP4TKXPFXExb5dqaxGBiXacZQzo3oBeJzyq4Is3t3CkAcUydhaPHdznsY+JR80Vt1dMLWGCQ0fuzuB8KKvD1mGmywgh4LFTzqOotraF8xgathYFM2n2CVnrWTBTUqHcLv0a+OYtYI06FpFGJYp8uigbhMvr0ssdEhN3YiwkMizkaUCxTFA4PZ79KPYPpRXpGEr3NwmPdV20HrrgoG1dvUPf9SQqtA0k0ZxH4Tyu07FYJhkKZoyqtmbxaKtq6w6l8bX6wekVJ1omYo9Hwyt1Y50uExq2zraDeGwPyXm0QcFMxJxHo8RjHCFrPQtmRCuexj2B4h3aH3R8jJ4ef9g6UXdIrIvC1HYotjCgTU/CFcRGNQiPdz8OGK7cVm8PLjrTC7s6jwnmPIo2PWv7iPPYydXWfcl5bAvKY/GKEB8TkndUE/7qPcVq51FzwhP5hUaN24upSXir7XMeDXGP5aKUisAs63jwV/Kn6tsEnE7QG34OVF/HGoLU6wSfrW/Ll4RDngaJRyQiaiN97ugatnbF7hbv3KT/ehLZh5Q7a+BnsF+kxalaRhnQpkev/pNGwDmPfW08IaFWXHsz2HkMIlc7mhA2CltndXUehRtq5cjESNXWVopH/+i5kIIZI6qt+5UrJzR6rXgLC/TKeQw3u1rbjDtm51En8aidkawHtg1bJ+I8GtPjMbhgJg7ncedG/dcTVDFP+9ARW/7sDU8BF91paM5jPCIt1SFhWLr+bXoSEbW0JnOcR2NULYtHu+U8alwAr/ggZboNWweqrS1u1aPNeaS8TCtD6d1VW6fZN+dRX/FYEQjvxYte68oReY0aAbJxWeD5YxWPPh2qreniQogpncRa4mFrfUcT6hK2DjeYQC88cayrv0nOY6w5teJCRJggOpPIeMKh6Yq4a/EClTpH+t3q5MtYRe3W6T5s2suL4emSseMJOeexD4lHEbYWooSJ3OPRDtXW4Vr1+J1Hq/KGHJqcx67Oo2UztyOFJY0omBHicU/8RQX6XJg4Aq1VtAKR3u+bV8QpHkUBiA6TXOj4EH+n1dNJjMp5jLefopE9HuMJWxeWKjnW5NQnclHUHUHiMTWO9kEGjExMMLdQFMusb6eqAusLZvJSlPGNg9L1m8ATS85jZhpw1v4O9FezaeKBxaPVUP83kRfnF4+q88gFMxHmWncXtrZAFIU74WmcR2ODE92kQohmyyHV1vL3hStpNpHCkiJsrecFgGh+nYB4FPsxoXVRqJ5OwuEmyIhJLrFWW+sRtjZgxF3i1dYRCqqszMU0VDzGGLYWIevdW3Xtgxn2AiDW7eVPgWi0XcGMf6a1ziHreEXtCPXjd3cn0OxzmJ7zePq+TvztvFS8/3+pGFse3/OzeLSL6+ivttaErVk8Rpe4rnVNrHD6unEeZaFmRcsL4TrSyUmIa7ovQkpW9Xr0C+0IBTMum4WtxX50JeCIdjdBZt0i5aJj15bY3D89moTrne9I6FYwY6ecRwOrrWONmvjzHY0JWctrovGUdKFDxPLZlW2weFRv4xGPYqb1ujaHLcTj8PRAw3Kj6M55LFcP6f4FDrxxbSpmjoh9u9irFr8vt+mhDzdx8lDD1r6+3ueRXNkpByu3JBjFiSXUAdDmjZH7qFMILspFBsRjkPOoXVNawMGyqkG4gFxIVzoksWZT15QZOHmHhCUD/RR1CluTm19QkrjzqD22UuI8tkIrrbWQcHvixuDXiSk8nMCFiQFOUcARTXTCjN7V1nGKRxJPYv8ZkfMY67r8ldbGFMv4of1Iw5pjch5zDQ1bJ+I8js5Q2/TYxHkcqa5H78rvaJuEF+Yor9vhllCQ7cDLv0vBRf/y4utVkrHi8cIzjsHlF5yMkuJCrFy7CX/48+NYsjzyhII55xyPC047GuX9S1BX34j/fvId7n3oOXR0mnmSt/toQs0liHrfl+qy3hqedAAw/TDgs7nAjrXmvvbIacDRFwV/j8RQqCAit8PnA5xORTwaeDUXVhDR62oKUmS0IsOKopnQBuEqjvZWSFS8YYV4FOKfju9QsaR33ipVWosLsUSEiB4XJtk9TJCJw/nTZQqIAZXNCeU80kWi6Jmqd7V1vDmiwnWktAqd3dCYcx4pilE21HDnUVmXRzne4ymYabVfzuMo9eNwvZHOozP63xkuRiUaIGajaRJeoHoLf37Xh/1GOfCLyU7cclIKjlqlSVnogZi1yfFHzMbt187B/Y+/jCPPuloWjy89eieKC8M3Qz7p6INw81UX4P7HX8FBJ1+Ba//4T/k5/u+358f60r0TUfkq8h2DigcsrNQVzDhCCZWcdQO80w83N39P5KzRFT+5RxRyXTm/y8McVlZcizY9tLZQYSHcRkvFY8inkwiti3WbyfDJym19VdefqTmPkl5ha3/IOsEJHCQ6RAgv3v2YK8SjfnOIdcl5NKLAQRuWjzVdgy5oRJ6uzpNc4p4FbmSbnlhzHosHKGYDXTzHM2ozFvy5q7EUzOTbxnnslyrh9GIfMp2S/G+wQW164u3zOEJ1Hje0m9Oqh67LtBSq12g76yTc8KKyr8cPBHJjSIWPWTxedt6JeOnN/2HuO59i3cZtuPFPj6KtvQNnnajObQ1hxpSx+HHJKrz14ZfYXlmFL+f/hLfnfYVpE0fH+tK9vMdje9cTqZU9Agn6IO83QLnvTIH3sLOx5+CzzasgLixTbn/6FPj3TcDfLgXmPat/NWU8U0tO+z1QNCB8mx6BlS2EQhuEC1R31PywtQOYeaRyd8nnXX+su/OoQ7GMuDBJtF1Pd2Frq6qajXIehaiNZ23+udaBEa36O48u+7TpibVVj8h33LUZoLxEI/F/nsbhPBolHhG9w/enwRJeGi1h/iQfjitUtlWtB6iJ3lgzNucxQ7ndaGDYWohHIi1kN1KomqhvAXY3AJurJaQ4HdhrePTriend7UpNxeRxI/Hw06/7vydJEr5esATTJ48J+zsLf16Nk395MKZOHCWHtgdXlOGw2TPw+vthTiAqKQ4HUkQo0GBc6ptD3JqNLyNLflM43B3+NUiSF27VebRqXfI6igbATTlone1I+fpNeA85A82jZyJt8iqk/RR5/+mFu6hMdjpTGvYgpZvtQNvIqwq11PR0OA3eZu79joM0eBxQVIbUr95U9l97S5d95fa4lfWnZ8Bhwn70DRkv5/o51/8Eb2Y26HTpcLcHrcvX0SqvNyUzu9ttqvvaRk6Dp6i/LAxcK77rsj1SJK9ycnClIU2HdblLByrbvqYyob+Ttp3H64HkSocrLb796M4pVNbS2qjLNpf3p8Z5jHd7ubPzlXW1N+u2Lm2PQFdaenDlbg/4cvKVY6CtWZdjIGhdqhhyprqQGsNze/OL5feRs7k+pt+LZk1EquRV3qepaT1+1nsqRoA8cOfuzbquJdy6aD/SsZFKnydRvJbkTIFbTTlwdbTo/nlH69KKtJ6Oj+EZSrRgYhZkESnyC9N0ND5c6hp8aoePdCc9f89/d5pDwsA05X2yzZ2CtBR9BaR/XVJAQ+WkpwRdbwjnsaXdKQvLhRuAoSXAvqNSMH+NhE7tRWAEYtqSRYV5SE1NQXVNsIW/p6YeI4eqV/ohkONYVJCHt5/5MxxwwOVKxXOvfoB/PvVaxNfJzchAYZa5YbXSXGsqm5sLikBTZdN9HgwoUBwKT1YmtqnOY0lurlUd+dA8dIyytrpdKN/wI/b0H4SmCQcgs185itS1GsnWov7yB2uZpxXpPbzeNvXkUFJQhIxOA3KTVCSHA1tEk96iAZCOOE++m+7p8O8//5p8iiAqzM1HRpux28ubno2tp14ji4ny1/+K9vwikFeSJXlRqllXjeQF+QKZuQWG7UPJ4UR7/+FIr94Kp+qiV+77S3lb5K+aj6JsckWDG5W709OwXT2JVuiwrq2lg+Rjp7S9ARkJPt9W9cKkpLAI6b7YB+NW5hfLabjFcCNbp23uVkWaIyU17u21I6cAtHf6OX3I0mldEoX46Z/DiQGFhUhpi97pa+lXBkpoSHe3o1znY7NeFbFZmVkoieG5q8uHgzJm89obUWjA+yU/zSX/zWnpGT3+zTsqRsr7q7ixCjkGf/6mSD75/VqSn4+M9p5fy5OVL5+zyDGuyEiDI0P/aItH8vr7KfZ0zPdPp08/D7Z1OjEoTRGSlT76bAmfXpcIGXLbs2bkpbuiev7hLg+cjlq5RU9aTgEqDDrDF8rFZ8q5cHBxHupbAmKyMFtxh9NT8lBR4MT6nXRktWP2mFTM/SYbm2p6HsVpePxx3xkT8dtLTsPN9zyGxcvWYOigAbjrhsuw+9JaPPDvuWF/p6m9Ha2d5lSnkkon4VjV1AR3FGpbb7xu5TU7Wluwo14Ja0lt6t/udKKqpRUesyt1VTxZSsjGvWuzvDapSTngWiSgTV2rUUhpGfCqYZDqbRvgCC2SCXUW1PBPdVs7nAauzVdcLq9NhFnFGjuaG/z7T+BV81jrOj3wGby9vFOn+kNfu0bvAzQrYba2puB1SWr/vBafcfvQO+kAeI++GKjZCddbD0FKy4RnwAg5FNY6/320hQnfpkjK2iklInQ7xnXsqBNd9mxeA0do6D5O16oqzmOrU+2aULt7B+p12Oa0piI1vyyR7dWpVuTXVO9CnU7rki/C1Urdnc2tcDRF/7zeQcrJrbOpPuFjIHRd6eo+bPX6on5uMmrc/ZVQcfP6pWjVeU20rRqalZM7fcJ3ty7az25KkyEhvGEFGhqMee+KdXnVz7fq1raojnlfWqBYprK+zpB1lahVHjRhpqd9mDdE2d/nrnPioDwHLiv14oVdXt2Pq9LcXNS2KRWaXndnVM8/uUARsxvb6O/QLw86dF1VTc1yNXW6y4HalkbsrA8U0GSkKe+1NTsb0dQOfLwM+L+TnBg30IOqKN+zMYnH2rpGeDxeucpaS7/iAlTvCX/A3HDFuXjj/c/x0lsfyV+vXr8FWZkZ+Outv8GDT74qh71D8UoSvCYLORKO0Vi1uqO2JpE62wKvrymecTuclohabdGBb/c2eW0p6rp8KS54jF6TmGPd2gh3FA2DRcGMh5L0jVybqHas3ACs+A4gkSSLsqYux4/IlaM1Gb69xu7tv+sbvw+wZqFyv70laF0pqginSn7D1lSsVjoXD4D7vNsCyf0rv4e7IfwVrUtcHKSkotOn6TMXD4X9ldumOrh1yOfz70dHnMeWmvPoobw5nba5tmAm7s8tNefRQ8Ugeh4LssvngpsclVieVxWz4d5LiZKuPp/PmRL9cxeUKlOtvB54tq4x5HPFq3bWkJw97EcSsZSv2dIAd+0uGI56Me6hvPdo/m7RVq6l0bDzqCiYoSgvpZKIcHE4ilRls6PDh3u2O3DPdgrlktbQf23tXuWzKsURXbh3sEt5/Lr26B6fkKbxOGSx6HCSvlG+XyjvKic8Xgk1NK+RdFklUNvsQFGOA2MqvFgcRTF/TImFbo8HS1etx+xZkzXdFRyYPWsKFi1dE/Z3MjPS4aM2KhrE1/S7fR7RJFxbFRvaHsQqRLVz9bbgKSCJNEyOtVimLkxlrpUFM+Vq0nrlRmDJF8DPXypf76ns+lixvYzeh9ROZJBagFazU3m9sTPDV1v792G6gespChTnUNW3SPT/4cPIv6NnayMdxhKGPbbiWRcVJonf07XaWpNLGE8TegPmWvuJtxLcoNGEcRcYDRkXuFAU7xu9ifbYGjhKud0euSWervhnp6fYokE4oe1l0V1xClVXZ6nLNqJAJtGCmeEmFMt01yhc5Ds2hARkfliv/CEzR0a3rpirUp544W2cffKROO24QzFy2EDcd8sVspP4yjufyD9/8K5rcJOmDc/HX/2A8087BicceQAGlZfhwH2m4vorzpG/Hyoq+7R41FZbE3o3TY5nXTRHVTuhQ1SBp5opHndH9XDTWvX4JzxsUG4/eBp4+lZg0cddH2tWyyVyHckh2LYG+Obt4JNkaMhWnTBDBSCGISpU5z0DLPokMIavSr0ICUfQBVOa9WMJwx5bafFXWpMg0rN5fbzzh8POtfYYIx5jFbUGVH8n1NpIiMctq2AY0TYJH6heHG43qddurNvL4EprrfPYk1ArVpfc6QOavPYTjyNMaNPTXaNwUWld1xJePO4dpXiM+VPn3Y++kXs6Xn/5OSjpV4gVazbinCtux55aJU5eMaAEPk0omvIaKTR9w5Xnon9psRz6JuF438MvxPrSvVs8apuEC6FGosMq51GcgGmai3ACRO6lkcJDIIRr1OIxAXcoWsjVLB0UcB5lJGD3lvCPN+sCYMI+yu3K74HVPwCHnhFw/zrbIvSeNHBNorEy9XNctUBxHHvokyePRKN9SCcrvZzHRHs8irWJdirxrMuANj1h5w+LsZN2aOgcbxshI53HeCbMUEcFw8WjO0rxONIa5zHa7WVEz9AQPJoMN7ldj6978ai4jsa7e+4Y+zya6Tx2qLsxQ16csgELVOexnooXwojHWVGOKoyrYOaZue/L/8Jx6pybg772en1yg3D6ZxuGT1ImkmxeYSPxGHKSt7pRuD9kHZgL7DBDeNjZeSwbrHyY0gdk6HztcOjduzDSdiI3lJwoEo50u/hT4KDTlJ+HFBpRSyhDLwDIAQ0d51ZfHd2vet1KD9FE0yL8YesEZlqHrCt+8ZhvjHgksS2mKiXiPBrg8sXVIzBoNKEBzmOsE3morRRdBNFF/I71uq8npnQbWguJffr8pR6PZhDrmEmDp8sQWn+8O6HWz2VeyFrrPFIVeE84IWGY+tG7wYRJaOHC1mK6DPV41LJ8m4S2Tsk/urAnLJ9+Z4lYO+Vq4MzrA/lrViJO4uGcR8KqRuElqsNWpTkBW5LzuDtGd8jAtYnjJcq5sg7/9jJwH45TC2U2rwwIgZ++CDiMoc3L/e6xQduJqoApXEkn6xhPJE49xDZNzhFh83B5qGaLR/9oQgOqY+Od12x0jpp/OkkPwoOOwSMvUJruE1k5BoatY9xWwnUk4ahnukHE6ERqzyFr+tzRuXl6zzmP9nEeyUV0+3oWj0WpipqrcZsrHlOj0FwVadQPUgmpb7NIPApxWB+a0eQFftoUffP5vice6SCnkwA5JEdfIjc3tRQxCSRSzqMZQi0cIjwrimWC8uUMXhMJQCEAoi6YMcHlE/mOlEAfDWasiSqrCe3YRnJuPngS+PF/QOV6c3Me89SQNbV7iHEKhv8En4jYLh0ScDsTaNETdl3xiEcRwjdCPPpDiynxO48GnOyjFmpjZgJ7HQacejVAvVMNDFsHtpUrtnzHrathKNE4j/58R5NC1jZ1HqPNLwwOWxuPO4bZ1iPVQOPmjkBzcSPpVGP9aa6uzmNdSNhaG7qOhr4nHsUHFFE6CL69j7HHeMKQqlj/SLQU+4StTcmXIwpKAq5ZlCcSU3IeB8TmPMItUg8M2l75/ZT9RH87FaRoofzHT17sKuCMdo/9s4BjH+emyzHfXxWPOob3EhpPKJzHFv37uQUKU+IJW+cZGLaOMkSs9i2UP09OuSrQ7sXQnMcoxdDgscbnOxL+mdvOwFzvSJXWO8wTjzE7tTYSjyJsvcdjTjcXdwwFM8PVYpmNJhTLBOU8himYCQ1bEwvWS9hUFZ2A7HviUXxAiSbP+x6HTurnZRV2rLamBssksilEog39mdHmRRuyro8uZG1KziOFQ4vVk93OKJpgEUavqZ/aT5H2UZQum0OkRxi1D4XTFo94FNsrEWFbporH3fqJR6f/wiTNNjmPCYetDRWPUa6rSH2fU09P6qcoRtIa0qonhtnW1KeU8nbpYjnaKEO89NSiivJAxeeOmc5jtKkHpoatoxNqosdjrUlh684oQumCEerpnkYlmkGd+lYqznV0adUTWm1NfLVKwuzborNs+67zuG0tsH6J/Ibdc+CZah2SzaqtrSqYKVVdR2pGq/lwCxTMuMwRj7WxiEeDnUcxkpByMKM9uRnd51E4N7E0DfavyWjnsS4Bhy+BtfVXm7hHqoCPg4RSIkTxUBzbI/qLk0QKZizMeRTN3D+bGyjsot6gBuT1+T8fonFpRciaxJrebYxCEbnakT4nhOtIbafaw5ztjSKWCxPq5SrWbsTFSJiK626dR5PD1p4YCmbMdh4r65TXK1c/lrXiMbTaOlb6oHhUtxwJgHnPyg5fBwkDIVgsE48hR5NZfQujLZYhPMK1SrNVsYwpYf4Yi2WC1mSYeOwfu3j0V1unUZd+48SjqLSOgYQvAOhvEi7NLh3Foz//OAHx2FJvs7B1rn2cx/U/Ae8+plSP61QhH7HPYzRCWzTc32pwyFqumvcpf7fYXuTcX/VP4IzrlPtCPJLZYSaxiEfhYtMFgFHN1GPJeXQpD9pjds6jo+fHDlcDPhs7zHEeK9WP4fJCR499HmPF8NnWtnUe25sDPQxzC5V5xZZWW7eHzZezRDyGK5aR16R+MFBuDok0o6oQ4xGPRjuP5SNC+jtGgdGpB37xqI7/iwbtnHRaV6z9Ac0IW8e7vUoHK8cmuXw65hjGPb2IPlPE54oBYWtal5Rw2NqiPo9UlU/bhoQTFTfRBdATNxhTLKPtixmN0BbjNau2whTouHemK9uLci1p2wyfDFxyd+C8YGK+o7KmGMLWJuU7xlowU+s2K+fR0eOaQsPWZjQID3IeC3tu1RMrfc95DE3KNmuEXDjojSleN0Q8+tu8WJHz6Hcet0WeAqKn+zh+X+Vqm6ov42gQbrh4pAIe+jCPtfrS6F6d8TiPWmfAiLzHRApmEnXb/fmO+rmOCa1LuI7kyGhFu9XNuI0OW0cjPMSx21AdCFNTZwUKWxuB//MhNYa0GRNmSGtD13RxIlJRaL1kdFBRnJmTZQSx9MU0Kd8x2spmIR7t5jxmOCXki5nbxhq0firrwjmPym19K4etYyOkHYQueVbxonU7Qx0gq8LWlL8iKq1DKlblpHN/OxUdt9eYGcrV9nG/AipGAfnFcYhHA7fX/icoH6Ibl8UmTIy8AKDtT0UGMZ7kHEGNwvVel0PjPCaS8+iyTb5jQhcmBk2XSXiGdIaBc62jbcgtxGMM7/GE1hTttqJ9Jjui3qib2+uauyrSLj5/Ffj2XaWYiN7fUbYsszRsbYrz6LBdq57OKFv1FKYEciQbveY6j/0LAKcDSE8FstIjV1vHQh8MW6uyWyQfW+k8CvFIawhNEve3eTF5XdRTjKoe6QOruS5sI2efPAVER9dK9HQkMXP6tUrokdyaGE5shjmPdJKbOFu5/9Ub+jcAjhdRbEAf2DEm0pOrLfd51Nt5pCbPtP3phBeHYEpYPArnUecpHHFfYPrFowHFMolMchEne3L5jCgIiWZdcRTF6RO2TlFyfSP1IPU7onvMa8itzdcWHRQoZYgmoC37qsuUKFPQbi8bOY89Fcy4HBLyhPPotlfBTJG6rjqTxiYSVQ3U/FuCK8WB0vzAYe/1SWhKMHTe553HQFWzhc5jaL4jkWj+V7yI/mYRwrOGhNOpNZDYJ+SKxOFI6DKdJByzT1TE9LqfYiqWMTz1IJ6Qdei20ls8ipA1nUTiOPEm1BKHLmiEY26XsLV/uowBPR4TadVj4BhAmWgqm/3OozmhYf/FZU+fEeKizKyQNSHWRj1/Rdu4GrVFGjmOBlcwJ+TUGj2tKILLF2mai3AdvRLQ4DU3bJ3iIEko9dxCyCRHlPBJwO76QOg6MNc65hkOXeh74jFDU20dVJhihXiMMJrQSkdUiMdtPYhHvUKe2lnIrz8Q2C8xhmkMcR5pRrKY4PL1m7H/vpEV4CK8FcdJzt9yySjxGEe+Y8IFM7Sv6ERHxw+5RjoS97El0i+Mch5jHSHX5WRvkCjxh63t5DxqlES34XST8x0JcdyLY5jMBCNaOxmVT2tBwUwkl080CCeBJpnk7rk1Iqy7cHqhuinrTRSPoUUzBVn6hKz7pniM4DwaOn+4p9GEYZxHw9u8hF1PRiBvbOuaHho56yQ8qIkyOXv04U4VhW88qMyT/fmLmJ5G/5xHB3Dw6Yq4Xf1DfG6WKJgxorWRcG5q4hCPRk2ZyYu/TU/Cx7w4bnVs0ZNwn0exptDCM6tzHg0cTRidI+qIq6NCQkQtHs3NxQzaXtQtgKiJoXuCnaqtzSyYcUjdF8uYFLKOTTxKpjuPoUUzhTn6FMv0vZxHEgJ+57HFf8KSrHIeI7XpIawQj1SsQh8WlCjeWNN9yFOv7ZUbMgt52xrg+Ttjfx49nUfaBsdeBoyappx0vn4rvucxcrZ1QmFrg5xHkX4Qp2uSkPNowGQZgcMTx7FFnzVlQ2ObSBTrurzisytO8WiUU9STqKX3PF240HtLZ5c4EvI5nT67aB92Jx7NrrQmxPElWqSJkLWVxJJPa0W1dQ9hazMFWqfappNIcwItmq/Dh63NcUTDNQpvVNNn9XAe+5Z4FMLRbgUzoaMJg9q8mChqB4/psR2N7q6VCHUmGNrz58slKtTo7zrpt8CIKcoH6HuPK9Md4sHIiTzx9Hg0y3mMO2ydwAWAQZXWcYtaCkFSWgoVOxjlJMXbJNzI0YTRTJgRoWG6SDWrKIWg44uOrUhimwppRJswK8LWJTYSj7G06jExbO0vmIkQMy1W3T2z2vQQ2iM4qrGJZjuPmkbhu9T8x0QbhPe9sLW20poqQq0SaT2NJiTECd6oiSnhGDwuCvGoc9g6gYkkhoStT7lGEY60T17/B7BqgfVrCucc0YUQHcNxtPAIiMd02zQITyhsTSd94droXGkd934coI6z3EWuo2SvghmjT/Y9ravQgtBwNIU81PqKzgN0HEaIvBi6LnExZ4uwdZTHFjnsRqdBaHCHzJE+vdiHG8t9/vdYsfoWrTHV3XOEmW8tRcx5VKqtzXceB1DOo06jCfue8xia70hY2Yy7u2rrREaixQN9cIkTXjdjufR3HhMTHLoWzFBD3mETlA/OV/6s5F4mglE9REUj4YaauKb8GF5tbXbOI20Pl+ryGVCAEVfYesBwQ0PWMYvH4y8HSiqAVT8EnD+DnEdHT+uyoiiF6OkzQoSs66sSL0WNBe3wBbuIx2jnkwvhSJOCaGqbKX0eJVWkSXhsuNKa5/16B5a3BuZa15qY8yjC6fQpT+vq75KwYLIPr+xx4MYtTtuIRznnMVs/57FvikdtXzwrcgujEI8Os51HynekD3sSJN3kIemf8yhCnYmFrXUpMPKLn5rEhSMh1kQFQfQhrFeILoF8RwQ1CTeq2trknMegwhTJXs5jjO2d4quITel5QtKEfYOLMgwNW/fQI9Bq5zHS9vK/ryxalxBhZm+XeC4AQvMdqe2TCYJbm/NYkAJ/T8dp2RKWtzr8oWEzw9ah65qRJ6EiDTixSMKNmiyaIosLZkrzlH9EvQ6DnJx93Xm0dAygONmG+xA3e/JNDy16ksd5TGBdYmKLXiEr7ShAPS9OEsh3DE490PHYov6covVUwuIxzqpmA4plCGes66ILPhFGN9R5jDIvbeAo5ZZEyaYVSroDHZt7tluzLr/zaJV4dBlyUaaLeCTX04jG7bESrXg0sdI6VKSRQBNMVR21fi7J1Okyof0nqYXQEPVjsCzkMAs4j+YWzNQ0Ae1uCU6nA2PKldeua+awdWyEVFpbEh7WUqa6AFVbzZ+LrIU+TIdOiGp2s+5iO4FxdmGFh/h74gjn+teSYP6lHxHuJGg/hktPiIcET3KGVFtrL4Ti2fZB4eG0+N5HBuQ7Bq2L8rvoZNrTyb1skPI4OqEaWU0c7QmeogrE2kXAZ68oVfHkhhvVS7A7h4/yU0UjbJMahHepTo+YiynaB5ksHrVhazsUy8QSth69l3Jr0ihHTyTxmCWFtOoxV6C5NesapK4rJwXIckpo9TksLZghdtYBw0qBISUO3ZzHviUeu8l5lMwsTBFvyu4mYpjhiI6dCUzYDxg6MeAa9SAedc+XS7CxdNgJEiTU4hEwwnlMcC3+NVGbWmoFJVd4ptmiQbhh1dY67Ee/wxfTuhyBljgGVFrLr6A9luRjyxNlvqOBIWvtMe+M0nncvs7YpuXRVOrKRSnqNqQUGTPp0RG1QdjaDvmO0bratC+nHqLc/3Ge+c5jesA9myL7QpJlAs0tCmac5DwG1lXqAjZ3hBtPaC476yQMKw0Iam7VE3fOo0Y8WjVhpqeJGEbnYu53HHDQaYGvyW37+cse8210FR40Jk38fXHMQu5ywqJwHLlD9JxhCtiNbnId6QSviEed9qPcTqQsoRONbs4j5exSDh3N4dUhdzWuZtyUz0chc3q/7Kk0ocE0ra3N+mKZaE/w6ZmBi1QhHq1sMB1UlBKhIZ5h63JH3l60VjqWLAlbu20oHqNwtfc/Qfk5zeDuwXQwOmxdkAoMTQ9MmDGzSXgX51HzsVqmikcnJHmNVjqPWrjaOlYyuqm2Njts7W9qHCZkbfSEmb2PCQjHH/8HLPsmatdG13w5ESZuaUg4z8ffBJgEUaytS3R2QYPWJZ8YMvW7OKETHP19dNzGXdWsk3g86kLFuZYrLVsSFt6BY8sV+/uIBKxBPQMdomcnHfPRvB9Nch79oqO7JuHlI5ULKrooNKEPX4+j7axy93oS29RpgQQktegy2pntLr3FLmFrb09FT6XA5AOU+1+9Yb5IcwJFIUubnh3IK7Qq59GlyXkUziMhhCNRZ2Jr09CKa/8a2HmMs8+jJufRsoKZ/kMi5zsSRvUInHEEcOiZyv0vXwO+ey+mXw9sLx3C1no7fUI8xrvNjHAe9bwIqBgJ/HKOcr+aGpfHd/UYmG2d4DE/SG0qT/lzomVHApWigQpPV+zvI4PyHf14hHhM69mNLe5vkngME7am16dqelH5OnCkua5jT5W6IrfaoBSDuJ1HK8YS2tl57Gk++eyTFGG54Wd9OlPEKB5TZedR+aLdB2Q4gUPyA5+HZoeG3VIgNC0ELFEmF/AEqsAbPZS3aW4+prbimvD6JDTpkH7fx8RjOOfRorB1T+PUxAme3qB6tXmh8NXh5yr3v3krZuEoL0fPsLVwHvW60k9EcNMJJTvfIOcxwePLoc7ZnnV0oNDh4//E/XS65K2SSBE5ov++SakuJud2yedxP2VcrraBk2XiOrb6D1OcPkpFMaoVTqTcQipEuew+YP0S4M2HlO9VjDZdPEZ0reh4Gz5Zub9moXnr8a9LbC+XfSqttduLIjDaNnJW0t0FQHF5oPWTia5jaMFMufox8XkDcHQhcJgqHiks7FXiBaaLxxEZwRf0ouLan+9ogesY6jw2tOrTValvVlu3hymYMbXPI03EEBWiEU56oUn6nTocdeJES/Oj45zXrG/YWp8ej34ScfnEWsi10V5cJIguzcvH7wPs80vlPqUYfPIfoD3+cjmHR4c+j3QCEfuOxjfGO8JRuy5xzJPoIAEWTU6cKJaJ9D7Si2iPLTP6O0aqaqbXppP9mBmKw7dlFVCuhtC3r4VpRCqYoclN9LlBAi1SxMWUausU+1Raa/ejXVxH2laRqq3pa4p+0PuTLgCMdvzDNgkPznn8oM6BowsljMpUvq4xOd9RK2pHqK2bBSXqx0WhhfmORGWtpGuxDNHn+zxa4jwWliiJ7OQuRvrA0LZv0EvYCqcvgXwjXcPWurfGiaPgInQtOrcvcehxfIkcusWfAv99IiHhqNua+qniUcciFf+6or04oZYz1KSYTnSU82gH8UipBWYUy4Rzh0TqAHHQqYobTJ8z5GYZVUwUdl0RxCN1dyBW/whL6M55FBdDVjiPJOwplWr1D7ANkZzH/Y9XjnE6pj550fRlCYeP2uCIfMIP64NdRrPzHbXrGhnBeSxUG4RbUWkdGrau06FYpm85j3TFRB+koTmPIjxspvOoTfKP4K7Q20H3Ni+5ohVNjQ4hTx2dR53C1nJlM92JZ3vp3SBcz5xH0ZpHJ0HiFMe8aM8Ub7cAnRP8u7Rb6qkvpsh3JGEUOuJNZ6I6tsiNiWI+vH5rChFp1L1AUD4COOQM5b6ckyZZKzxou42YqtxfY5V4jJDzSLm7Q8ebJ/pDodd84Apz91E87jH1C93vBOX+vGfNnf8dUpgyRJPvSNXM69tJuFknHjt9wc7j7k6gLI3+SZb3eBR9HVs7JGSlO9h5jNt1JLGmdW6sKJjx5ztuMWbiRo8FIQm0UzGi2lpv5zGe7WVAsYxu+1C4IjqFtXQpEhPiUYdwtbYvZpdIAN3S9CMSZlblOxLRXGRSiJiK8uji1IywdWhVc1bI+NVhE5VbEwsalHWFCXlSriNdrFAzaZNDnX78KSQaQUTb7uiLlPtLvtD1eI4NGwnHLnmrDiXH+fhfKznXlDqzaoElyxIO3xBVpFXKb0sHlmgEUY3JDcK16xqofmz9qAY3hTsa6PFo/tpC3UcWj3HnO7YGv1GNGNWWYJseXUfu6TwKULc2L0a0xtEj51HHYhldxCMdl9RGJIFxhBH3YSLOo3BD9T7Zhu7DA08GzrkZmLh/5PeRGWIkmv0oxBr1vTNhzm+XamvhPP4wLzg1x8x8R+26tLmF4ywOWUeqTqd+t3RxRn1mP3/FsqXZDuFqi/2473FKm7C6KuCj5y1blkd1+ES+4w71o+znFoctwtZOdRkL1fX4w9bqW8GqsLW2aKauVZ/PJmefznc0cv6wHs6j3r0edQjN6tbaiK5kRRqBHQpmDHMeE7wAKFJFGo2606mQx18xL4/bi2Nb0d8iGirrnUsXug+Fw1kxoutjDZ4sE3Zd3W2vYZOU203LYIlIEzmP1IBbdFIgF7DSBBc07LpSA/vSH7K2MK8v1Hkk0UiiiPj4hYRziXsVwtUW+1FcGH39hn5jVhMQaYIdnQ5biUfBwmaHv1AmzSFZXjBDbK1WFlndoM/z9Z2cx4jiUVuYkmZ47pTcDianQGmsXLXN+MIGAT2H2AYJiLXgnEd6g8R5FSOcPgqxUYWzHsQzoUTHfNBut1e8FwD+kLVBhSnkPra5Y3cdSXhS0+k2ndvRhF6cZOUFbwcBHcv5xeaLx0j7MT1LyTMkNi2HJSJNOI+tzUolLFVf03Gj1/sr3kpdEtV0oUjjCM0Wst1tryPPV+6v+8laR9SOaPOPKWonDA+TJslEK9KUsDXwkyYUa/Z0mUAVeGBxP9Npzac0M6eK6yK1YMZK8fjIRz7UtgAvf6fPZKc+Lx7lDzr6Rx908gnL4D5bIsmfQpBa19PonEfhrHW0AR2JtHkJcWp7+BvMrG6WC4zEuizIBzWkz6MID+vYxsNBeb8khuItxjKg0rqreFT3oei9KV5TIE5mVB1rghMSOLYibC8quKB8MNomZhUShDZVFzmPJOhp/77zqDnriLgu9fQy0uJCmXDNy0nMDhkfcB2ZIByUdiHGvVJBER3blK+qczeKxJ1H5XanG6hyKzmGNRbkFXZq9FiHD9hF6/Eo4XUKXdsh53HrHuDP7+g3ErQP5jy26DMWzeCQtW4NpnUWa/41JZonakSOYbwuHz1ehPx0dh4TTj0wQDzKCDcqntzV4grDxGOXMD+14hEOpLaaWBTLmFV80dOFnAjrmRWyltcUMgXE7zwa3Jw81kpd0fvSYtcqSNSKz2ESRNTQnYm8vYaoHQR2mNhovoc+j6HikaJg/6l2yFXOC5qtWBf8bKUBT1DWQpB4FOMJrcx51Ju+Ix4jha31Fmk9MXB0VMUyhEPMPNXDefSLtZrEr0j1mFBiRF/FeJ1asW3IwdI578kvhuIV2n7xqHduYQKTgoQLWLPDwKrmNOWCT9sqROs+mpnvSPR0EWB2vqMcNdGIIcohFmuzWjxqK3VpTSJv1YqRhJGcWrOPn2REXJxQtwOzpxT10IxbsKMjICZv2OJExSInKtU8SCvFI0FOKFHqkixv1WMEzr431zqMeDSrUTgVrAxXTzLrFvf4cEPC1nqEZYVrlWpT5zHWnMc8Yyqtg46tePIwaSyhGJumt3hMxHk0MmytPeZFyFqgzXs0a6Z1NOKR9hEVENFjzHTXtE6auDim/RpvKokRlbok0mh9JGgt6AsYscDI7OMnGRHbS0zfMbtqP4awdQBrwsJurXhUxetutWWQNmzdm8Rjn895JJxeD7xmVFtPPVjJIaFWHlFMMtBVPOop1vSYb21EjmG8IeJcYyqtg/ZhPCkR+SWKQCc3Tu/m5e5OJYcvVvFIQkCcTIzoiafNeQwVj0K0kssmRLXpzmNa5JA1nVyFc2q2eMzSFMvYqVJXFBHZweHTOo9+N5TFY4/pBwRFZKq3w27ikXId7YAnjPNYra5tWAaQ5ux9Yeu+Ix4zQhroGtX4OhIUwpl8oHL/p8+i+xU9HVF/mx4dBJI4QcYbtqbtTEnYxB79PpDiFtsGtekJWlM8zqMIWdOFht59A+N1Hkk40rFM7yPqjac32gsAke8Y6jyKfDWq3tVxDnl0LarU/XjQacq4PW0XA7OqrAXaCTOiKl3v6vdEK3WFeLSDwyfWRVEo8d4yeiZ6MqPdj5XrzeldGoN4pJzC0BxIq+gMIx53qx9lYzMlf1FNi371KpbTh8LWFuc8jpym5PnRCXdtzyFrZV165jwm3iC8y0jHeMX2mBlKtWPdbn2nX0TrPJIAuuzPwMyjQtr0GBG2TiDn0YA2PX7i3YdGhqxDU0iE8yhcV/Ha/qIzEwVJaDidmkuT+0kXHrQNaXuaXU2sPblT+y+biEd/pa6YumM355GOH4oAUb51i05N73oj2uPLBvmOoeKxa8jaOtxB4lGErZWvx2ZqQ9b2ELuWOY8XnnEMLr/gZJQUF2Ll2k34w58fx5LlkQ+uvNxs/N9vzsPRh+6LgvxcbN9Zhdv/+m989s0imJ/z2I3zaGTYeq9DlNulXwWHA7pB11xMf2hWh/Bnoo3CJx6g3NKYKz2Jdj9O/4XiPBx6hlLgYHfnUe9K60ScRwNmWkfchy71PUt5hDRhhtxzClmbXWmtXRftRxGmrtoK/PffinBsqVfaYJmJtvOBEI92CFsL4aFtJm8D8RgoXku3zZpsjfY8tc36fMfQ8LCtxKMvcH+ruq4qOedRkvs89rZ8x7jE4/FHzMbt187B/939CBYvW4tLzzkeLz16Jw444deoqet6FedKTcUrj92FPbX1uOz6+7CzqgYDB5SisanZIuexyXznsaBUqcakq3GanxpzvlyC66K/y9+KRseCmXjC1iRih6qtH5Z/C12JRqhREcrYWcp9Cr8edaHS4NmANj0J70MDnUcKw0rxrMvfpsegGcDaC5PsgsDfT449CSQS1DG0uzJE1NKcZoKaS1spQLQn95xC2ziP/pC6OLuQqK7dbS8nzS6hdDsjthcdZzs3wH7Oo31cPLdmXdtCwtaC+r4uHi8770S89Ob/MPedT+Wvb/zTozjsgJk468TD8fAzr3d5/Jkn/gIFeTk4/oLr4fEoH3bbK6tgKrKLkd5zzqNRziMVyhAbl8XUU0y3ghkRsqZWNAk0CNelYIYcJAoZbVmlf3+1aPYj5VrS9qDjgMSjyL00yHn05632FuexxLgejxFzHmk0IwlIEo9UvSvC12bmq2nbU1WMDLyfrQ4PiwEH4j1udZuecEJNFtjW58uxeIxze9H+M7MQLEqRJqbL2GldlZ2U/xgcthb0aeeRXMTJ40bi4acDIlGSJHy9YAmmT9achDUccfDeWLR0Ne656dc48uC9UVPXiLc+/BKPPPMGfDSiLwwpDgdSqKO9TkjZeZD3o88Ll6cTDtFUl/4muq+KtJS0DKRofqbLa9OBNXE/+X7qz1/CGeXz07qEqHW60pCawLp8BSWQj9umWqQl8DzytlIFkTeO7SVvi8lKyDplxbe6bWuxrlSfV16Xw5Xm/14ongn7go4659pFcNTshPeQMwLP09IQdGzosS5tn8dYtr2UmQs3ucWSD66Gat3Wpd2H8nZIy4j62JLSM+FWhZtrzzbdtxWR4vOo60qHlJMvHzOpbU3w1eyEb8h4OMbvA4nEUksDXG2Nuq4hmmML5cOAtEy5+tS1a5Phr9/dmui2k44vZwocuYXytkppb9H9MyyudWlcUWfV1oQ+v/RaVyp8yueg+H61vsdwPGuK9DllFdp1uX0++Zhy7lhn6f7Trkty0K2yF3d7nAmdz/RakyslBV4HHe9ebO90+NfU5JPgk3xwqgZpvc+c9epxbHVq223pIR6LCvOQmpqC6prg0OeemnqMHDow7O8MqeiP/WdOxlsffIFzf/NHDBs0APfcfDlcqSm4//FXwv5ObkYGCrPUUKIOdPQbCPJJnB2tGFighsK061dFWm5OLgrD/Dyh1y4uR2VuERzuDlTUbYMzhudvUIVHZmY2ShNYV1NZBcjjy2hrwgAd/r5MhwRKOsjLzUdBDM/XXjoEO4v6y9ti4O71MW2LaMhPSwV52mnpmSgP89ySw4mtVCFLjVu3rUBG5XrsmHwA3MXlcHS2Y2BWOkD/dKRDPbZS0tJREcu26j8c5DemNtVhYA69F/R7PxDZToCSTHJy8lAc5bpaB0/AbocTrvrdGEhDW3Xef0SO0wn6dMnKykZbToEs2EqdPnS01YOSCqTBykVqZk0l+hvw+pEoSEuFHHgl4Ujrq1yHsvyQanCTKc3NxRafTxHbeUXytip2+JBt4naJtK6tkk8R23TeaNqDXIvXJK8jPU0+DxDOtmZUpPjgsMG2siO0rt3uVlCcqqR6K7JssP+IPHkEpxI16UjLRUWBCYM9othWHS4akdqIjR76nA98LtR5q1GszrV2p9DPcpPi2NpUU2N9qx6H04Ga2gZcf9cjstO4bNUG9C8tlgtuIonHpvZ2tHbq50l7Zhwr30qVm7CjPri9CKnzFPUE3+jxoTXk54niHacWh2xeiZ010YdpaV0Z6rpaJXRZd0xrSFEEUWdtVULPQ2uiA7JdLTpq8HrREsPzeWYdL9861izEzmr9cqDEuhqaGuWvO+EI+3f6hk2Ej1o2tTSgZuVCecaz9OEzwJk3AJUbEto2kdZVUKjsQ68zNabn9w5WcnS9e3boui6xrVpblJzjZp+E9iif3zNNuUD0bl5lyLaidbWoBR+tPsCnjtur3rUDUofqGVHKA504dui/v6I5tgQdaxeb8vrdramqqQk++oxIz4RXzemurdmNehusyyvC/OS4bFqFRovWpF1XrWYfSrs2o9IGa6Jt5Y7C5bFkXe8/hdT+w1C7cal8QWeHde1qDqSdLa1twQ6dJ4LFu62eq/dgU2MqFrd40egNHFc7O4FiVWVtb+k09TPL6GMrJvFYW9co5y1SlbWWfsUFqN4T/vCqqq6Dx+MJClGv27QdZSVFchjcLVqZaPBKErx6/dHUVkMNlUrz3wtrx6aqYWtfSmpUdm1MqMn10rqfYn7uTHVdUqLrUpPpfY01uvx9vk7lys+X4or++ag1z7h9lN9b+pX+25mOG3VdUqR1jVELZVb/CLc4uW1bAzx2HaT2FkPWFJgwE+M+VMdYSnsqDVmXT60M9qWmRf/8AxXXz7dlpSFr0u5DH71v1dGE7qb6QM6him/nJsPWEA6Pui7/Otf/rN9nVJzIJwZvsKj2NDcET3ixel3uTniqtgVa91iIOLYIyeTjp7ttZYd1hFuX/L5r+gl2ok2tmyA2t9O2c9hkH/rwkSyBgnN7d3dKmKgGjardPnR6e8+xFVNiIQm9pavWY/asyZriVQdmz5qCRUvXhP2dH39eiaGDB8iPEwwfUo5dVTVhhaPuzDpKqdyk6Q8kFMJg2HhCylkTfc42/Bzzr+vWJFzvUYDa+cPRMvUQRUDSlIItBo1w665ghgpWRk9X7q/8PvhnVIFuUEJ4XLOt8/sB4xWhjZXzDVlXzEVPtO9ElbORI/jEPqRtQFBhE11EUbW11mUwezKIduQfHcMG9ATVpQjEbgUz1fYQjjLaEylPlklKqj3AV43AO7VAo/00dxfEiMLeWDATc1XKEy+8jbNPPhKnHXcoRg4biPtuuQJZmRl45Z1P5J8/eNc1uOm35/sf//yrH6IgLxd33XAphg8ux2EHzMBVl5yGZ1/9AIaTkQ1MO1S5/917xrfECWXEFMURoKq+5tiNf92ahOvcxzDmJuHkIM08Urm/4APjKi+7q04fOQXIyFK2gYkNb/3HFhV5qO5Qj+xzrPL4jUuBnZuMWVis1dbkhFIRG027MWKyTKhIExc82ibOomURCcr6apiJ/wKToP1iF7SjAAmTJu70iPjsstMEF21fTDuti4kaCQ4cuiIFp6yhYhDrXceeqNIccnUe+683FmLOeXz3o29QXJiP6y8/ByX9CrFizUacc8Xtch9HomJACXyaMUaVu/fg7Ctuwx3XzcEnr/1TdhyffOk9udracGYcrjgm1GqgG+fPsFY9JB7jdB31bdWjs/PoUYVHapTCY/y+SisRev0VBjlpQSHikO1FjaUPOVO5v+I7U9uGBIkOEtvULqk7aDupaRb49l3j1hXrBcDgcca7jtoxgKLbArXpEVB7IGqTY0VvRa3wsLhFTxDa6A05s1EOIDC1zYtdIFeWogyUsmHyxQfTN6kKEo/oVcRVMPPM3Pflf+E4dc7NXb5HIe3jzr8epkKCYcYRPbqOhDPRiSlhnzRFaQxOrI8vb0SXcDoJT9EgXK8+hjEJDwewzzHK3R8/MvbkFuki4NAzgcJS5YTxnXGCrFv32C9qexCPex+jrJ/6YFKqhVHE6jyqVc7YugqGEpLbGOQ80jSgKQcC65fAdMjRo31J4t8m0za6OI92aRBOLP5M2Zdmj2zsBgd99jxxoypsbdB3kun17NZ8nPW2sLXh1daWMWE/ZaoMhdl6+AAzZMLMoNFKmJSck8pN1jmPwnXUq0F4rPlyFC6mkXbkiiz5HIYSLv2ACpZE6sL7T/bs/OmMg05SdBKlfdjTfszKU3JDCaNFrv8CIArxKI8EHGaK89hVPGqcx1ULlFnoBjRz7wlq5YT/3K0cP1oX0mq0Fyd2yXcUubpG5esmgsnvf6ZvU6WOKCRYPCYLIvS3+FPqZB6dw+fSMWw9YqomZB3fVa4uOY96h6yDQp5RCA9y0ggSjkbP/tUKD8qzJNFzzCXK1z/MM941625d8pSjHsQ25YXSY0ggbV5h7Jr8zmNadBdCFEau263PeMvu8HbjPOo1mz1eKu0xoi2ieLRLviPDMF2cxz4/njApIKerfITywSrnuMF853GkEI/xh9h0CVtTuJbQ060RwqOnddF+GDxW2Q8UsjYarXgksXbgKUoOIeXKffma8a/f07q6G1FIaQ4UkiXm/9fwJflzC6O5ABg01hzXkQiteg8Vj0zkCmI7OY8Mw2B9O9DkBbZ2AL4kKPCJhd4pHifNDrh+UXyg6t6qJ69YmUtMvS03xe8gJRy2pmrz2Scq9/Ws2u0StnYAM49Q3DJqYxJuP8RRbR4zlNNEbUGoqpn6BE5UX/9/z3UNh5qJf3t1sx+HT1LWTGIpzgKrmBA976IRj0NEsYzxzq3/mBeweIzBeWTxyDB2otHrwPifnGixSbcqPdFvgLRdIOEwcX/l/tKvo/sVvautxcl258aE8gz96yJXiv7FytEXK0KW8j71zKELLZgZNQ34xTnA6df6GzvHsx8SRb6uE9uM8hzT0gFqUGxVuFrgjcJ5FEKXqtHNqJgVgpb2V3fH1rCJwACR77jGhHV1k/PI9JDzyGFrhrEbO90OWUT2NnqfeCQHJ6dAOelE6eA49e7zOGS8crtlpX4uTKzCdurBAM1xppPLO48GQs16hjxFq56ywcotCVWRa0qig/YDOb9mOGmh4mPKQYGcV4sJ5NSmRXaISYATy8wR2kHHQ6R1FfUHTrhSuRD4+Utz8g27q7ZmkqfammGYXk3vE4+TVPFCuY5ROjiBwhS9xeMq/dq8xCIei8sVJ5D44lWlSbmehDqPxRWBn+17vOJmif1AFZdm9p4T4oP6e1KBThQ5r5bnPI7bW9m/1BOPnFIzoGNLjAwNF7pOzwJOvQbIzFZaBlHo3+xJLgQ7j93DOY8Mw1hA7xKP1JonDgdH17A1uTU00YWeM8FJJo54RwFSoQgJAmpm/MP/oDv+ghmX4kr1K1e+pnzD/GJlJOToaaaGrMM6V8u/sUdrjp5aG4nc0GXfmLYkR3cV1xTGPvEKJW+X3MY3Huo6Bs+M/SdGEzLRbS8WjwzDmETvEo8UpiVBQ05bDA5OYDJJavQj5HrKd6R2K6EuSjzEWjRDoWMxw/nTl4xphqv9u9IzFJEh2uEQB52qiF3aB2ZPmNCKnEXWh6x7dB7pYoOmppA7a+D0nagbhdMaT75K6Y9JP3/9AaDVPPfPoT2+2HXsGQ5bMwxjAb1LPIqWImsXmZdbaFC+o59Yp9/sdZjSk4+qvPfs0GcNXdak2V79Biqim6p3v3pD6ScpBLhZ+Xvh1kbbX8xCtrPz6K9IX2qqSAsrHml9p12juPfkeL/5kDXj5dzqPuR8x9jC1tznkWEYk+hd4rF8eMD1i3f+cEJ5j46A87hZL/EYg/NIj6FCGWLRxzAl5DlA3eY1O5S1ih6FVjhpRIM6s9aMvpJR4hDzh8M5jzT3W4TYzUbb7J3Wdvp1SqEThfpf/Zt1M5zFxRw7j9E77ZS/SmF+hmEYE+g9fR4zc4HCsrh6GgaNkCP3Jd5BKCUDlRFzdPKlNj06QMJWilbUkhChvE+a4RznPO2YhAeJDtHGhRpxE0u+AEoHK19b4RzNe1YJnxs5F1qvPo/URL2gRNmW602sSA/X7J0cUGroTmMkSTjGeAGm77pE2Jqdx6jFY3tzj5O0GIZh9CK117mOFKqMp7ditPOHu2OoGrLetka/CuNYnMcZh0c9klG3E7xfPO4InMw+fBqWQaFfs8O/PRGpCb2YQkQhdj3yY+Pdh9QPU7RY+vZta4VjkPPI4jFq8cg9HhmGMRFnrxOPlRvNyS0MhwhZJ9iiJ651DRwNlA1R3CTqyWc0no5AwQdhVH5lb8C/D0MuAEaqFenr4x9hqYvzWDY0ULSz3AatjfzOo80uAuwsHrlYhmEYE+lF4nGEclu5wZh2Kj1BRSKiYIfG9OlFOOcxXEU4FcoQlGdoRu5T6AxiEbZmoquYp/QCEmyEmU3Uw+1DMVNbHudpA8EmmpHzBUkMziOLR4ZhzKP3hK1F4Uac4jGQWxhn2HrwGCAjS/kQr9oKw8TjeX9QZiA/fWughyEVO4gQqBmuY6h4pPuiUIXpgkNUD2vdY2qFQ1Xxu7eaM7mlO+eRhCyx1IKinXB88JSSt7ojsT6pfQJyrcfMAH7+wuqVMAzTh+gd4pFCp3QCJBET74SORMPWY2YG2gTpmG9IowD9ojanUAlPy683I9BUmnItaaIKtcmJN2wfb9iaqNnJyfqxOo9C7FsVsg7dh3TRY3SRVbSQc26ngic7U18FvHiP1atgGKaP4exV+Y67N8dfqBLOHYoahyLmiDULYVixRdmgwPcnqC1eCNEUfO1iY5qC9+Q87tluzmsmK6EXJjTBhWawExssFI/Um1MQwzhPhmEYpm/j7F35jhv1b6cSDQNHATkFimOiZ76jvC6Na1U6JPD9IROU8LXDAYzaK67m6PqJR853jCn1gI6XjGylICTeHF1d1qXZhyaORmQYhmGSm94Rtk60WKa7dirRIFzHdT/p795o11Wk9rEkKF9u/D7KKMbsPGW6xNbVMD1fjuDChtjEo6iypgIVK8P9HWrOLOVdWjFJhmEYhklKnEmb4zjtkMBkDGpKnaB4pNzC+MXjTGNC1qFtXsTfKRzGCfsFQtaUO2dm2FHrWtllDKBN8U8womOLnOLRe1mf70isXqCs4eMXrF0HwzAMk1Qkp/N41EVKT8V9jweWfqXMVqYQYMOe+J8zXvFIVd75xUBHG7BpmXHFFlm5Aefxi9eUggtq0E0TSswOWWvD1iSM6qrMfe1kQ3sBMPkgZRISTXIx4niJhaY64LX7rV0DwzAMk3Qkp/PYf6hyS6LtgJOU+4nmjgl3KNacRxGyphCkdka23m1eSKRSf0c64ZPTJ+YOy1XmHebPIRZh61qqtPaZ+9rJhjguaHTlwacp9795K9BqiWEYhmGSiOQTj3nFSlsaao47/7+BJrnbE+wJF6/zOFaErH+EIQjnsV+5cit6SFJ1rICEo9nj7UQj8nhbI/VF8ZhbqDjI1duBRZ9YvSqGYRiG6SNh61K1XQ25b1+8Ciz/Vmm4nGiT3Hha9dBaKARJLtyGpTCEUFFIxQ2iLQ+FyklImx2yFuKVKoZpog3TPaGO9EcvcFschmEYJmlJPvFYMlC5JfdGVPrqUO0bKJiJIWwtilU2LQ+uPjZSeAjnkdb7v+eAQWOAVT/AdChn79t3zH/dJMR/bBErvwe26jj7nGEYhmFMJgnFo+o8Vm03Jjwcy2xrf39Fas5tgvDQOo/C/dOGrxl70takFBhRbuhnL1u9GoZhGIbpa+KxQrmt3mZM9XC0YWvKvaTCHZ/P2JYrWueR3M26Xca9FmMIDkovePFupa8iFTwxDMMwTBKTXOKRxroVlweHrY1q5NwTo9RGzzvWKc6SGeKRilN4hnRysnOT1StgGIZhmD5YbU3NwamnI+XbNdbo+9yx5jyaELKW0YatRb4jwzAMwzCMRTiTstJ6z3Zjp4D0RHoWMHiscn+d0eLRHT7fkWEYhmEYxgKSSzz2G2hMsUysfR6HT1IcUKryrtsNI/GLWoKdR4ZhGIZhLCa5xGPpQMOcR794jKbaerRJIWvtuqhSV+88T4ZhGIZhmF5dMONv02PAVJNoC2aoaGf4FHNC1kRDDbByAdBQzePsGIZhGIaxnOQRj2kZQEGJct8IBy60VQ/lV04+EFjwQXB7FZplnZEFNNcDlRthNA5IwDuPGP46DMMwDMMwvSts3U/t79hUG5irbGTBzGHnADOPBM6+CcjODwjKoy9W7i/7hmLJuq+DYRiGYRjGzjiTLmRdnfgowu5zHl1AZm6gmpraA515vSJeT/u9Mkt68wrgqzeMWQfDMAzDMIyNSU26Yhm9J8uEikeHExg7E3A6gdpdgCsdKB0MXHK38r2aSuCtfwI+rzHrYBiGYRiG6W3O44VnHIMFHzyJjQvewH9f+BumThwV1e+dcOQBqFzyHp7+xy3xt+kxquJY2xJnwr6B0PTLfwZamxThSLev3q80KWcYhmEYhumDxCwejz9iNm6/dg7uf/xlHHnW1Vi5dhNeevROFBeqeYERGFheilt/fzG+X7Q89lU6HEDZYOMqrQmvR2mHQwwao9yuWag4jS/dB/z8FTD3r0B9lTGvzzAMwzAM0xvF42XnnYiX3vwf5r7zKdZt3IYb//Qo2to7cNaJh0d+EacTj9xzLf7+r5ewZUccTbXLhgCZOYrjZ5B4dNB/bo37WLNTEY4iVP7Bk8CuzYa8NsMwDMMwTK/MeXSlpmLyuJF4+OnX/d+TJAlfL1iC6ZNVty4Mv//VmdhT24CX3/4Ys/aa0OPrpDgcSKEwsYp32ARQhqFj+xq4nCTzUqAXrpQU/20n5T2mpctfO9ctRqr6MyvQrssu2HFNBK8ruddE8LqSe00Eryu510TwupJ7TXqtq9Pr1Vc8FhXmITU1BdU1mr6HNPClph4jh6o5iSHMmjoeZ554OI4443dRv05uRgYKs7L8X+8aMRltAAqrNiO/oABGUJqbi60+jyxSif471yDdoNeKdV12w45rInhdyb0mgteV3GsieF3JvSaC15Xca0p0XZtqaqytts7OysRDd/8e19/5MGrrG6P+vab2drR2KtXPkjMF7rLh8v3G1YvRXF+v6xpJndNGrmpqgrezQ/lmYy2q1y1TQtkWoV2XO4qrgL66JoLXldxrInhdyb0mgteV3GsieF3JvSYz1xWTeKyta4TH40VJcWHQ9/sVF6B6T7AbSQwd1B+DK8rw3IO3+r/nlMPOwNaFb+OAE3+NLdt3dfk9ryTBK/7o8pFKKLmlAZ7dW2AU8kYWU2bW/Gibg4HWEY2F3NfXRPC6kntNBK8ruddE8LqSe00Eryu512TGumISj26PB0tXrcfsWZMx7/Pv5e85HA7MnjUFz77yfpfHr9+0HYeccmXQ9278zXmyI3nbX55A5a49Pb/o0PHK7ZaVMJyqrUDxAHV6DMMwDMMwDJNw2PqJF97GA3ddg59XrsdPy9fi0nNOQFZmBl555xP55w/edQ12VdXg3n8+j45ON9Zs2Br0+w1NymjB0O8HMXSCMsWFGKIW2Gw2QTx+8BTw+VzZ5WQYhmEYhmF0EI/vfvSN3NPx+svPQUm/QqxYsxHnXHE79tQquYgVA0rgkxKc+XzWjcBHzwNLvwbKlXxHv5g0Epoaw8KRYRiGYRhG34KZZ+a+L/8Lx6lzbu72d6+57YHoXuSQM4GUVOVfXRXQEEWIm2EYhmEYhrHfeELD2fAz4EoDDjvbvHxHhmEYhmEYJknF4/tPAq2a1j4sHhmGYRiGYWyBPcUj5R1+8LRy3+czp1iGYRiGYRiGsbZJeEKsWwy88yhAfYq0LiTDMAzDMAxjGfYVj8RKpZckwzAMwzAMYw/sGbZmGIZhGIZhbAmLR4ZhGIZhGCZqWDwyDMMwDMMwUcPikWEYhmEYhokaFo8MwzAMwzBM1LB4ZBiGYRiGYaKGxSPDMAzDMAwTNSweGYZhGIZhmKhh8cgwDMMwDMNEjQOl06XoH84wDMMwDMP0Zdh5ZBiGYRiGYaKGxSPDMAzDMAwTNSweGYZhGIZhmKhh8cgwDMMwDMNEDYtHhmEYhmEYJmpS0QvYe68JuOKCkzFp3Aj0Ly3GxdfcjXmff+//eb+iAtxy9YU4aJ+pyM/NwfeLl+MPf34cm7bu9D9myMD+uO33F2PW1PFIS3Ph8+8W4w/3PY49tfX+xzz7wB8wYcxwFBflo6GxGV8v+Bl3P/gsdlfXIlkwa1st+OBJDCovC3rtex58Dg8/8zqSCTO2174zJuKNJ+8N+/pHn/N7/LxiHZIBs46tSWNH4JarL8CUCaPg9frwwaff4Y6/PYXWtnYkC7+5+FQcc9h+GDm0Au0dnVj482rc/cCz2LBlh/8x6Wku3H7tJTj+yAPk+1989xNuuudfQduion8J7r3lcuw/YzJa2trw2nuf4Z6HnpO3C1Har1B+jsnjR2LYoAF46uX3cPtfn0QyYda2omOOjqsRQwciMyMdO3ZW44U35uHf/3kHyYRZ2yvS59aUw85DdU3geeyMWdvqH3dejTOOP6zL66/ZsBWHnHIlkpFe4TxmZWZgxdpNuPnex8L+/Ol/3IIhFWW46Jq7ccSZv8P2ndWY+9if5A8Igm5f/tedkCQJp112C0648AakuVLx3EO3wuFw+J/n24XL8Ksb/owDTvw1Lr3uXgwd1B///tv/IZkwa1sRf3nkP/IHifhHJ65kw4zttXDJ6qDtRP9efPN/2LJ9V9IIR7O2VVlJEV55/C5ZcB577nU458o7MGbEYDxw59VIJvadPhHPzn0fx55/Pc789a1ITU2R/3axLYg7rpuDww+chV9d/2ecfMlN8t/+1P03+X/udDrx/D9vk7fR8Rdej9/d+gBOP+4wXH/FOf7HkACvqWvAg/+ei5VrNyEZMWtb0cXHM6+8L//+QSdfgQf+PRc3XnkuzjnlSCQTZm0vwezjfxX02bWntgHJglnb6ra/PBG0jaYfcSFq6xvx34+/QbLS6/o8Vi55L8jxGD64HN+8+zgOPuVKrN2wVf4enYh+/vR53PfPF/DSWx/hoH2n4T8P345xB56F5pY2+TG5OVlY9dXLOOvy22SHMRxHHDRLPiEOnXUyPB4vkg0jtxU5j/9+8V08+eK76C2YdWzRB9jij57F0y//Vz6BJSNGbSs6kd9wxTmY+osLZJFJjB05BJ+9/jD2O+4ybN4WcDGTiaLCPCz//EWcdPH/YcHiFfLfvezz/+DKm/6G9z/5Tn7MyKED8dXb/8Kx512HxcvW4JD9p+P5h27FtMMv9Lsg5516FG753YWYdMi5cHs8Qa/x+pP3YMWajUnnPFqxrQRP/v0mtLZ14Ko/3I9kxajtJZzHsQecicamFvQGzDq2jjpkH/nY2vuXc2SHOxnpFc5jd9CVN9HR0en/Hp10OjvdmDltvPIYVyroPETfE9DjfT4Js9THhFKQl4OTjzlYtrmTUTiasa1+c9GpWP7Fi/jolQdw+QUnISWldx1uRh1bRxy0NwrzczH3nU/QW9BrW6W7XHC7PX7hSFC4iYi0PZOBvJxs+ba+oUm+nTxuJNJcrqCLi/Wbt2N7ZRWmTxkrfz1j8lisXr8lKHxGIbW83GzZje2tmLWtJo4ZjhlTxuH7RcuRzBi9vT6e+yB++vg5vPLYnZg5dRySGbOOrbNOPFx+zmQVjkTvOpuHQezom666APm52XClpuLKC09Bef8SlPUrlB+zaNkaOWRB+VhkV9M/yrsiB6i0X1HQ893yuwuwfv5rWPnVy/JzXHT1n9Bb0HNbPfXSe7j8//6C0y69BS+8Pg+/veR0/OHqi9Cb0PvYEpx10uH4Yv5P2FlVg96CXtvqmx+XoqS4UL4Yoeeg57r5qgv8+X3JCDmwf7z+Uvzw00o5B0r8LR2d7i6OTnVtPUqLC+T7Jf0KuuSWiRNYSZJuCztsq4X/ewabfngTH750vxzSJFc8WTFye1VV1+GGux7BnGvvldO4Knftwev/vkfOSU5GzHoflpUUyW5lMh9XfUI8kit4ybX3YMSQcqz6+hVs+P517DdzEj79ZqHsaBC1dY1yLiPlNaz77lWs+WYu8nJzsHTlevh8SsKr4F/PvYUjzvidnB9BP3vwT9egt6DntnriP+9g/sLlWLVusywe7/z7U7j4zGNld6m3oPexRQwoLcbB+07Dy299jN6EXtuKQt5X3/YAfnXeSfJzLPn0BWyr3I2qPXWQ1OdJNu656dcYO3IwLr/xL1YvxfaYsa1Ouuj/cPTZ1+DGux/FnHOOx4lHHYhkxcjtRUUl/3ljHpat2iBH4H5/x0Py7aXnnoBkxKz34WnHHSqL0XmfBYoJk5HecybvBjq4Dz/jd3L+gsuVKp+k/vvC3+STkuDL+T/JOVNFBXnweL3yzl3yyfPYumNX0HNRkiv927i1Eus2bsOij57F9MljsGjpGvQG9NxWWhYvXys/H1VgayvZkh29t9cZJ/wCdQ1N+OjLBeht6LWt3vrwS/kfVW+TU0kh7MvOPQFbujn+7Mrd//crHH7gTJx08U1BTjOJYarspNCX1vUoKSpAlepyVO+px7SJo4Oej7aJ8rM69DbM2lZ0MUJQKJKe49pfn4W3532FZMOKY2vJirWYOTX50kfM3FZnnng4Xn//84h5tslCr3cetTQ1t8onrGGDB2DK+JH43xddT9AkDOkg2X/mZPQrysdHX/wQ8fmoykqbz9Wb0HtbTRgzDF6vNygvpDeh1/Yi8fj6e5/3mjxaI7cVHUskHk848gA5tPTV90uQbCesow7dV64sF4JFsHTVenS63Zg9a4r/eyOGVGBgeSkW/bxa/nrh0tVysVBxYb7/MQfuO1Xebms3KmG33oJV24o+45Px892q7UWf81V7kqd1ndnbat8ZE+XiwZeTPGTda5xHahFCJyLBoIoy+SCub2jGjl3VOPbw/eV2FZScOm7UUNx5w6WY9/kC2eUQnHHCYVi3cbv8uOmTx8qPodCrcMnoymLqhFH4YclK1Dc2Y+jAAbjhynOwaWul/yBKBszYVuTETps0Bt/9uFSumqXE4j9eNwdvfPAFGpKsKs+M7SWYPWuy3OcwWXNhzNpWF53xSzk81tLaJn9I33r1xXJPtWSq+Lzn5stx0tEH4qKr75bfIyVq/hQJayoAoltKXbjj2kvk5P2mllb5JLfw51VyhSdB223txm34592/x58eeEbOBaXWMs+++j463QFXg/YBkZ2ZIZ/g6Gv6OUVOkgGzttWFZxwjH5uUn0vss9dE/Pr8k5KuxZhZ24tC+tt27JbzA8mdO/vkI+SLPeqMkCyY+T4kzjrxCCxautqfU5nM9IpWPZGalc5991Ncc9sDuOSs4+QE+37FBXKS72v//QwPPDE3yDampPvTjz8MBfk52FZZhRde+1A+aQnoyoJOZONHD5NPkmRnf/7tIjz45FzsqkqeKy0zthUlTN9z868xcthAuVKNPmDIpn/ihbe7vJnsjhnbS/DIvddh4IASnHDhjUhGzNpWD951DQ47YAayszKxftN2PPb8W3jj/c+RbK2MwkH5nK+++2lQc+ITjjpQbU68WG5OrE3OrxhQgvtuuQL7TZ8ku7DUnPjuh571NyeO9FrksOx9zBwkA2ZtK8rJPvfUozC4okx2/qnPKvVbpZxtbXW/3TFre11x4ck45+Qj5YEAbe0dcn77Px5/Bd8tXIZkwcz3YW5OFpZ8/Dxu/esTeOnN5DQIep14ZBiGYRiGYcyhT+U8MgzDMAzDMInB4pFhGIZhGIaJGhaPDMMwDMMwTNSweGQYhmEYhmGihsUjwzAMwzAMEzUsHhmGYRiGYZioYfHIMAzDMAzDRA2LR4Zhei00lzhSI2CrsOOaGIZhYoHFI8MwTAgXnH6MPOkmXjIz0mWRSFN3GIZhehssHhmGYQwRj2djvxmTuvzsgX/PxbBZJye4QoZhGOtItfC1GYZh+hw071Y785ZhGCbZ4NnWDMP0CmZNHY87rp+DsSOHYFdVDR599k2UlRTKDmD51OPkx5xxwmE45ZeHyI/JzcnGlm078fQr/8Xzr33of54FHzyJQeVlQc/93cJlOHXOzfL9vNxsOST9y8P2Q3FRASp3VeOlNz/Co8+9CUmSMLC8FD988FSX9f39sZfw98deln9XuyaCciCfeeW/mL9oOa779dkYVFGGFWs24Ya7Hsbq9Vtw7ilH4fILTsKAsn5YvGwNrr7tAWyvrAp6/mkTR+O6y8/G9Mlj4UpNxZIV63Dfw8/jxyWrdN/WDMP0bdh5ZBgm6SEx+PK/7kRNXQPuf+xlpKQ4ZSFVXVMf9LjzTzsGazdsxUdf/gCvx4vDD5qF+265Ak6nA8/O/UB+zO1/fRJ/uvEytLS248EnX5W/t6e23h+OfuPJezGgtBgvvDEPO3ZWY8bUsbjpqvNRWlIo/25NbQNu/NMj+PMfrsQHn36HDz6dL//uqnWbu/0bZk2bgCMO2hvPzn1f/vo3l5yK5x+6DY8+9wYuOP2XeO7VD5Cfl4MrLjwF999xFU6/7A/+391/5mT855E7sGzVetz/+MvwSRLOOP4XePWJu3HSxTdiyfJ1Om9xhmH6MiweGYZJeq6/4hyKo+Cki/8PO3ZVy997/9Pv8NlrDwc97pRLbkJ7R6f/62fmvo8XH7kDl517ol88zvv8e9xw5bmorW/Emx98EfT7l517AoYO6o8jzvwdNm3dKX/vP2/Mw+6qWlx+wcl4/Pm3Ubl7D97/5DtZPJJgDH2OSIwYWoEDT7rc7yjWNzXjr7f+Br+bcwZmn/BrtLS2yd8nYXzVJafLDqd47H1/uALf/bgU51x5h//5/vP6PHz+xiO48crzcNblt8W1XRmGYcLBBTMMwyQ1TqcTB++7F/73+fd+4Uis37QdX8xfHPRYrXDMzclCUUGeHCoeOmiA/HVPHHv4bCxYvBINjS3y74p/Xy9YgtTUFOw9fULcf8c3P/wcFIr+adka+ZbcSyEcle+vlW+HVPSXbyeOGY4RQyrw1odfBq0pKzNDfs6995oAh8MR97oYhmFCYeeRYZikprgwD5mZ6X4nUMuGzTvwiwNm+r+eOXWcnFM4fcpYWVxpycvJRlNza7evNXxwOSaMGYblX7wY9uf9igri/jsoBK6lUV1L5a49Id9vkW8phE0MG1Iu3z70p99HfO68nCw0NCm/xzAMkygsHhmG6RMMGdgfcx//EzZs3o47/vYUKndXw+324NDZM/Cr806Ew9mzO0eP+XL+T3j02TfC/nzjlsq41+fzha/A9kb4vjATneqdO+9/GivWbAz72Ja29rjXxTAMEwqLR4Zhkpqauka0tXVg2OABYfMIBVQck5Gehgt/96eg8PZ+Myd3+T2qmg7Hlu27kJ2Vga8X/NztmiL9vhFs3r5Lvm1qae1xXQzDMHrAOY8MwyQ15NhRbuORh+yDiv4l/u+PHDZQzoX0P070VtQYjJTneEaYZuCtbe3Iz83u8v33PvoaM6aMw0H7TuvyM2rhQ8UsRFt7h/97RrN05Xps2lqJX59/UpdQPFFUmGf4GhiG6Vuw88gwTNLzt3+9hIP32wtvPX2f3NImJTUFF595LNZs2CrnKBIUbu7odOO5B2+VK6SzMzNx9slHyO19+pcWBz3fslUbcP5pR+N3c07H5m07sae2Ad/+uBT/eu4tuZ0OtdB59b1PZeFGgm3sqKE49hf7Ye9j5shV2lSYQ699/BEHyKHs+oYmuV8jfU9vyOW87s5/4j8P34Ev3ngEc9/9BDurauR2QvvNmIzmllZc8Lu7dH9dhmH6LiweGYZJeqglztlX3I47rr0E111xDnbu3iMLSmoSLsTjhi07cNl19+KGK8/DrddcLPeAfP61D2Tx+I8/Xh30fPc//goqBpTKPRXJnaQm4SQeyVE8+ZKbcNWc0+TK61OPPRTNza3YuHWH/HqimIW47o8P4U83/gp3XDcH6WkuuUm4EeKRmL9wOY6/4HpcfemZuOiMY5GVlYHqmjq5MvuF1+cZ8poMw/RdeMIMwzAMwzAMEzWc88gwDMMwDMNEDYtHhmEYhmEYJmpYPDIMwzAMwzBRw+KRYRiGYRiGiRoWjwzDMAzDMEzUsHhkGIZhGIZhoobFI8MwDMMwDBM1LB4ZhmEYhmGYqGHxyDAMwzAMw0QNi0eGYRiGYRgmalg8MgzDMAzDMFHD4pFhGIZhGIaJGhaPDMMwDMMwDKLl/wE+mY1gdGQTJwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split data Train - Last window - Test\n",
    "# ==============================================================================\n",
    "end_train = '2005-06-01 23:59:59'\n",
    "end_last_window = '2007-06-01 23:59:59'\n",
    "\n",
    "print(\n",
    "    f\"Train dates       : {data.index.min()} --- {data.loc[:end_train].index.max()}  \"\n",
    "    f\"(n={len(data.loc[:end_train])})\"\n",
    ")\n",
    "print(\n",
    "    f\"Last window dates : {data.loc[end_train:].index.min()} --- {data.loc[:end_last_window].index.max()}  \"\n",
    "    f\"(n={len(data.loc[end_train:end_last_window])})\"\n",
    ")\n",
    "print(\n",
    "    f\"Test dates        : {data.loc[end_last_window:].index.min()} --- {data.index.max()}  \"\n",
    "    f\"(n={len(data.loc[end_last_window:])})\"\n",
    ")\n",
    "data_train       = data.loc[:end_train]\n",
    "data_last_window = data.loc[end_train:end_last_window]\n",
    "data_test        = data.loc[end_last_window:]\n",
    "\n",
    "# Plot\n",
    "# ======================================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "data_train['y'].plot(ax=ax, label='train')\n",
    "data_last_window['y'].plot(ax=ax, label='last window')\n",
    "data_test['y'].plot(ax=ax, label='test')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fee4ed8a",
   "metadata": {},
   "source": [
    "Since exogenous variables have been included in the Forecaster tuning, it is necessary to pass both past values and their future values to the `predict` method using the `last_window_exog` and `exog` parameters when making predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9754fec0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2007-07-01    0.883929\n",
       "2007-08-01    1.041001\n",
       "2007-09-01    1.071707\n",
       "Freq: MS, Name: pred, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and fit ForecasterStats with exogenous variables\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterStats(\n",
    "                 estimator=Sarimax(order=(12, 1, 1), seasonal_order=(0, 0, 0, 0), maxiter=200),\n",
    "             )\n",
    "\n",
    "forecaster.fit(\n",
    "    y                 = data_train['y'], \n",
    "    exog              = data_train[['exog_1', 'exog_2']],\n",
    "    suppress_warnings = True\n",
    ")\n",
    "\n",
    "# Predict with exog and last window\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict(\n",
    "                  steps            = 12,\n",
    "                  exog             = data_test[['exog_1', 'exog_2']],\n",
    "                  last_window      = data_last_window['y'],\n",
    "                  last_window_exog = data_last_window[['exog_1', 'exog_2']]\n",
    "              )\n",
    "predictions.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "85d21adc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test error (mse): 0.06267633382927788\n"
     ]
    }
   ],
   "source": [
    "# Prediction error\n",
    "# ==============================================================================\n",
    "error_mse = mean_absolute_error(\n",
    "                y_true = data_test['y'],\n",
    "                y_pred = predictions\n",
    "            )\n",
    "\n",
    "print(f\"Test error (mse): {error_mse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "18bcabfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "data_train['y'].plot(ax=ax, label='train')\n",
    "data_last_window['y'].plot(ax=ax, label='last window')\n",
    "data_test['y'].plot(ax=ax, label='test')\n",
    "predictions.plot(ax=ax, label='predictions')\n",
    "ax.legend();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0dd12901",
   "metadata": {},
   "source": [
    "## Feature importances\n",
    "\n",
    "Returns the parameters of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d96b199c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>exog_2</td>\n",
       "      <td>1.520490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ar.L12</td>\n",
       "      <td>0.668892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sigma2</td>\n",
       "      <td>0.001597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ar.L11</td>\n",
       "      <td>-0.152130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ar.L1</td>\n",
       "      <td>-0.163030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ar.L5</td>\n",
       "      <td>-0.192486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ar.L3</td>\n",
       "      <td>-0.195244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ar.L9</td>\n",
       "      <td>-0.212033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ar.L6</td>\n",
       "      <td>-0.212089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ar.L8</td>\n",
       "      <td>-0.215658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ar.L10</td>\n",
       "      <td>-0.230837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ar.L2</td>\n",
       "      <td>-0.239330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ar.L7</td>\n",
       "      <td>-0.249651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ar.L4</td>\n",
       "      <td>-0.285973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>exog_1</td>\n",
       "      <td>-0.536553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ma.L1</td>\n",
       "      <td>-0.977181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature  importance\n",
       "0   exog_2    1.520490\n",
       "1   ar.L12    0.668892\n",
       "2   sigma2    0.001597\n",
       "3   ar.L11   -0.152130\n",
       "4    ar.L1   -0.163030\n",
       "5    ar.L5   -0.192486\n",
       "6    ar.L3   -0.195244\n",
       "7    ar.L9   -0.212033\n",
       "8    ar.L6   -0.212089\n",
       "9    ar.L8   -0.215658\n",
       "10  ar.L10   -0.230837\n",
       "11   ar.L2   -0.239330\n",
       "12   ar.L7   -0.249651\n",
       "13   ar.L4   -0.285973\n",
       "14  exog_1   -0.536553\n",
       "15   ma.L1   -0.977181"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature importances\n",
    "# ==============================================================================\n",
    "forecaster.get_feature_importances()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e96fefe0",
   "metadata": {},
   "source": [
    "## Backtesting\n",
    "\n",
    "SARIMAX models can be evaluated using any of the [backtesting strategies](../introduction-forecasting/introduction-forecasting.html#backtesting-forecasting-models) implemented in skforecast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e85edc9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information of folds\n",
      "--------------------\n",
      "Number of observations used for initial training: 159\n",
      "Number of observations used for backtesting: 36\n",
      "    Number of folds: 3\n",
      "    Number skipped folds: 0 \n",
      "    Number of steps per fold: 12\n",
      "    Number of steps to exclude between last observed data (last window) and predictions (gap): 0\n",
      "\n",
      "Fold: 0\n",
      "    Training:   1992-04-01 00:00:00 -- 2005-06-01 00:00:00  (n=159)\n",
      "    Validation: 2005-07-01 00:00:00 -- 2006-06-01 00:00:00  (n=12)\n",
      "Fold: 1\n",
      "    Training:   1992-04-01 00:00:00 -- 2006-06-01 00:00:00  (n=171)\n",
      "    Validation: 2006-07-01 00:00:00 -- 2007-06-01 00:00:00  (n=12)\n",
      "Fold: 2\n",
      "    Training:   1992-04-01 00:00:00 -- 2007-06-01 00:00:00  (n=183)\n",
      "    Validation: 2007-07-01 00:00:00 -- 2008-06-01 00:00:00  (n=12)\n",
      "\n"
     ]
    },
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      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
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     "output_type": "display_data"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_absolute_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "</table>\n",
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      "text/plain": [
       "   mean_absolute_error\n",
       "0             0.056244"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtest forecaster\n",
    "# ======================================================================================\n",
    "forecaster = ForecasterStats(\n",
    "                 estimator=Sarimax(order=(12, 1, 1), seasonal_order=(0, 0, 0, 0), maxiter=200),\n",
    "             )\n",
    "\n",
    "cv = TimeSeriesFold(\n",
    "         steps              = 12,\n",
    "         initial_train_size = len(data_train),\n",
    "         refit              = True,\n",
    "         fixed_train_size   = False,\n",
    "     )\n",
    "\n",
    "metric, predictions = backtesting_stats(\n",
    "                          forecaster            = forecaster,\n",
    "                          y                     = data['y'],\n",
    "                          exog                  = data[['exog_1', 'exog_2']],\n",
    "                          cv                    = cv,\n",
    "                          metric                = 'mean_absolute_error',\n",
    "                          n_jobs                = 'auto',\n",
    "                          suppress_warnings_fit = True,\n",
    "                          verbose               = True,\n",
    "                          show_progress         = True\n",
    "                      )\n",
    "\n",
    "metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "71c61f1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.904512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.932239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-09-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1.089341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-10-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1.113679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fold      pred\n",
       "2005-07-01     0  0.904512\n",
       "2005-08-01     0  0.932239\n",
       "2005-09-01     0  1.089341\n",
       "2005-10-01     0  1.113679"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtest predictions\n",
    "# ======================================================================================\n",
    "predictions.head(4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6733243a",
   "metadata": {},
   "source": [
    "## Model tunning\n",
    "\n",
    "To find the optimal hyperparameters for the SARIMAX model, the use of strategic search methods is essential. Among these methods, two widely used approaches are:\n",
    "\n",
    "+ **Statistical Criteria**: Information criterion metrics, such as Akaike's Information Criterion (AIC) or Bayesian Information Criterion (BIC), use different penalties on the maximum likelihood (log-likelihood) estimate of the model as a measure of fit. The advantage of using such criteria is that they are computed only on the training data, eliminating the need for predictions on new data. As a result, the optimization process is greatly accelerated. The well-known Auto Arima algorithm uses this approach.\n",
    "\n",
    "+ **Validation Techniques**: The use of validation techniques, especially [backtesting](../user_guides/hyperparameter-tuning-and-lags-selection.html), is another effective strategy. Backtesting involves evaluating the performance of the model using historical data to simulate real-world conditions. This helps to validate the effectiveness of the hyperparameters under different scenarios, providing a practical assessment of their viability.\n",
    "\n",
    "In the first approach, calculations are based solely on training data, eliminating the need for predictions on new data. This makes the optimization process very fast. However, it is important to note that information criteria metrics only measure the relative quality of models. This means that all tested models could still be poor fits. Therefore, the final selected model must undergo a backtesting phase. This phase calculates a metric (such as MAE, MSE, MAPE, etc.) that validates its performance on a meaningful scale.\n",
    "\n",
    "On the other hand, the second approach - validation techniques - tends to be more time-consuming, since the model must be trained and then evaluated on new data. However, the results generated are often more robust, and the metrics derived can provide deeper insights."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8dd0091",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,191,191,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00bfa5; border-color: #00bfa5; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00bfa5;\"></i>\n",
    "    <b style=\"color: #00bfa5;\">&#128161 Tip</b>\n",
    "</p>\n",
    "\n",
    "In summary, while the statistical criteria approach offers speed and efficiency, validation techniques provide a more comprehensive and insightful evaluation, albeit at a slower pace due to their reliance on new data for testing. Fortunately, for sufficiently large data sets, they all lead to the same model.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "608898c2",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(255,145,0,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #ff9100; border-color: #ff9100; padding-left: 10px; padding-right: 10px\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#ff9100; border-color: #ff1744;\"></i>\n",
    "    <b style=\"color: #ff9100;\"> <span style=\"color: #ff9100;\">&#9888;</span> Warning</b>\n",
    "</p>\n",
    "\n",
    "When evaluating ARIMA-SARIMAX models, it is important to note that AIC assumes that all models are trained on the same data. Thus, using AIC to decide between different orders of differencing is technically invalid, since one data point is lost with each order of differencing. Therefore, the Auto Arima algorithm uses a unit root test to select the order of differencing, and only uses the AIC to select the order of the AR and MA components.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f787da14",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,184,212,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00b8d4; border-color: #00b8d4; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00b8d4;\"></i>\n",
    "    <b style=\"color: #00b8d4;\">&#9998 Note</b>\n",
    "</p>\n",
    "\n",
    "For a detailed explanation of Akaike's Information Criterion (AIC) see <a href=\"https://robjhyndman.com/hyndsight/aic/\">Rob J Hyndman's blog</a> and <a href=\"https://sites.warnercnr.colostate.edu/kenburnham/wp-content/uploads/sites/25/2016/08/AIC-Myths-and-Misunderstandings.pdf\">AIC Myths and Misunderstandings by Anderson and Burnham</a>.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fa36408a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dates      : 1992-04-01 00:00:00 --- 2001-01-01 00:00:00  (n=106)\n",
      "Validation dates : 2001-02-01 00:00:00 --- 2006-01-01 00:00:00  (n=60)\n",
      "Test dates       : 2006-02-01 00:00:00 --- 2008-06-01 00:00:00  (n=29)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train-validation-test data\n",
    "# ======================================================================================\n",
    "end_train = '2001-01-01 23:59:00'\n",
    "end_val = '2006-01-01 23:59:00'\n",
    "\n",
    "print(f\"Train dates      : {data.index.min()} --- {data.loc[:end_train].index.max()}  (n={len(data.loc[:end_train])})\")\n",
    "print(f\"Validation dates : {data.loc[end_train:].index.min()} --- {data.loc[:end_val].index.max()}  (n={len(data.loc[end_train:end_val])})\")\n",
    "print(f\"Test dates       : {data.loc[end_val:].index.min()} --- {data.index.max()}  (n={len(data.loc[end_val:])})\")\n",
    "\n",
    "# Plot\n",
    "# ======================================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "data.loc[:end_train, 'y'].plot(ax=ax, label='train')\n",
    "data.loc[end_train:end_val, 'y'].plot(ax=ax, label='validation')\n",
    "data.loc[end_val:, 'y'].plot(ax=ax, label='test')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b546058",
   "metadata": {},
   "source": [
    "### Grid search with backtesting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a40122be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of models compared: 9.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "87bff93050e74ba0bbac8d12dc9b4c33",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "params grid:   0%|          | 0/9 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "`Forecaster` refitted using the best-found parameters, and the whole data set: \n",
      "  Parameters: {'order': (12, 1, 0), 'trend': 'c'}\n",
      "  Backtesting metric: 0.058789455086324986\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>params</th>\n",
       "      <th>mean_absolute_error</th>\n",
       "      <th>order</th>\n",
       "      <th>trend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'order': (12, 1, 0), 'trend': 'c'}</td>\n",
       "      <td>0.058789</td>\n",
       "      <td>(12, 1, 0)</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'order': (12, 1, 1), 'trend': 'c'}</td>\n",
       "      <td>0.058871</td>\n",
       "      <td>(12, 1, 1)</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'order': (12, 1, 1), 'trend': 'n'}</td>\n",
       "      <td>0.059517</td>\n",
       "      <td>(12, 1, 1)</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'order': (12, 1, 1), 'trend': None}</td>\n",
       "      <td>0.059517</td>\n",
       "      <td>(12, 1, 1)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'order': (12, 1, 0), 'trend': 'n'}</td>\n",
       "      <td>0.061720</td>\n",
       "      <td>(12, 1, 0)</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 params  mean_absolute_error       order trend\n",
       "0   {'order': (12, 1, 0), 'trend': 'c'}             0.058789  (12, 1, 0)     c\n",
       "1   {'order': (12, 1, 1), 'trend': 'c'}             0.058871  (12, 1, 1)     c\n",
       "2   {'order': (12, 1, 1), 'trend': 'n'}             0.059517  (12, 1, 1)     n\n",
       "3  {'order': (12, 1, 1), 'trend': None}             0.059517  (12, 1, 1)  None\n",
       "4   {'order': (12, 1, 0), 'trend': 'n'}             0.061720  (12, 1, 0)     n"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grid search hyperparameter\n",
    "# ======================================================================================\n",
    "forecaster = ForecasterStats(\n",
    "                 estimator=Sarimax(order=(12, 1, 1), maxiter=200)\n",
    "             )\n",
    "\n",
    "param_grid = {\n",
    "    'order': [(12, 0, 0), (12, 1, 0), (12, 1, 1)],\n",
    "    'trend': [None, 'n', 'c']\n",
    "}\n",
    "\n",
    "cv = TimeSeriesFold(\n",
    "         steps              = 12,\n",
    "         initial_train_size = len(data.loc[:end_train, 'y']),\n",
    "         refit              = False\n",
    "     )\n",
    "\n",
    "results_grid = grid_search_stats(\n",
    "                   forecaster            = forecaster,\n",
    "                   y                     = data.loc[:end_val, 'y'],\n",
    "                   param_grid            = param_grid,\n",
    "                   cv                    = cv,\n",
    "                   metric                = 'mean_absolute_error',\n",
    "                   return_best           = True,\n",
    "                   n_jobs                = 'auto',\n",
    "                   suppress_warnings_fit = True,\n",
    "                   verbose               = False,\n",
    "                   show_progress         = True\n",
    "               )\n",
    "\n",
    "results_grid.head(5)"
   ]
  },
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    "Since `return_best = True`, the Forecaster object is updated with the most optimal configuration found and trained with the entire dataset. This means that the grid search will yield the lowest error model with the best hyperparameters that lead to the highest performance metric. This last model can subsequently be utilized for forecasts on new data."
   ]
  },
  {
   "cell_type": "code",
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       "        <div class=\"container-32669b8d22ea4cd39f5bbe15d6f6f403\">\n",
       "            <p style=\"font-size: 1.5em; font-weight: bold; margin-block-start: 0.83em; margin-block-end: 0.83em;\">ForecasterStats</p>\n",
       "            <details open>\n",
       "                <summary>General Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Estimator:</strong> Sarimax</li>\n",
       "                    <li><strong>Window size:</strong> 1</li>\n",
       "                    <li><strong>Series name:</strong> y</li>\n",
       "                    <li><strong>Exogenous included:</strong> False</li>\n",
       "                    <li><strong>Creation date:</strong> 2025-11-26 15:06:20</li>\n",
       "                    <li><strong>Last fit date:</strong> 2025-11-26 15:06:46</li>\n",
       "                    <li><strong>Skforecast version:</strong> 0.19.0</li>\n",
       "                    <li><strong>Python version:</strong> 3.12.11</li>\n",
       "                    <li><strong>Forecaster id:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Exogenous Variables</summary>\n",
       "                <ul>\n",
       "                    None\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Data Transformations</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Transformer for y:</strong> None</li>\n",
       "                    <li><strong>Transformer for exog:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Training Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Training range:</strong> [Timestamp('1992-04-01 00:00:00'), Timestamp('2006-01-01 00:00:00')]</li>\n",
       "                    <li><strong>Training index type:</strong> DatetimeIndex</li>\n",
       "                    <li><strong>Training index frequency:</strong> MS</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Estimator Parameters</summary>\n",
       "                <ul>\n",
       "                    \n",
       "    {'concentrate_scale': False, 'dates': None, 'disp': False,\n",
       "    'enforce_invertibility': True, 'enforce_stationarity': True, 'freq': None,\n",
       "    'hamilton_representation': False, 'maxiter': 200, 'measurement_error':\n",
       "    False, 'method': 'lbfgs', 'missing': 'none', 'mle_regression': True,\n",
       "    'order': (12, 1, 0), 'seasonal_order': (0, 0, 0, 0), 'simple_differencing':\n",
       "    False, 'sm_fit_kwargs': {}, 'sm_init_kwargs': {}, 'sm_predict_kwargs': {},\n",
       "    'start_params': None, 'time_varying_regression': False, 'trend': 'c',\n",
       "    'trend_offset': 1, 'use_exact_diffuse': False, 'validate_specification':\n",
       "    True}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Fit Kwargs</summary>\n",
       "                <ul>\n",
       "                    {}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <p>\n",
       "                <a href=\"https://skforecast.org/0.19.0/api/forecasterstats.html\">&#128712 <strong>API Reference</strong></a>\n",
       "                &nbsp;&nbsp;\n",
       "                <a href=\"https://skforecast.org/0.19.0/user_guides/forecasting-sarimax-arima.html\">&#128462 <strong>User Guide</strong></a>\n",
       "            </p>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "=============== \n",
       "ForecasterStats \n",
       "=============== \n",
       "Estimator: Sarimax(12,1,0)(0,0,0)[0] \n",
       "Series name: y \n",
       "Exogenous included: False \n",
       "Exogenous names: None \n",
       "Transformer for y: None \n",
       "Transformer for exog: None \n",
       "Training range: [Timestamp('1992-04-01 00:00:00'), Timestamp('2006-01-01 00:00:00')] \n",
       "Training index type: DatetimeIndex \n",
       "Training index frequency: MS \n",
       "Estimator parameters: \n",
       "    {'concentrate_scale': False, 'dates': None, 'disp': False,\n",
       "    'enforce_invertibility': True, 'enforce_stationarity': True, 'freq': None,\n",
       "    'hamilton_representation': False, 'maxiter': 200, 'measurement_error':\n",
       "    False, 'method': 'lbfgs', 'missing': 'none', 'mle_regression': True,\n",
       "    'order': (12, 1, 0), 'seasonal_order': (0, 0, 0, 0), 'simple_differencing':\n",
       "    False, 'sm_fit_kwargs': {}, 'sm_init_kwargs': {}, 'sm_predict_kwargs': {},\n",
       "    'start_params': None, 'time_varying_regression': False, 'trend': 'c',\n",
       "    'trend_offset': 1, 'use_exact_diffuse': False, 'validate_specification':\n",
       "    True} \n",
       "fit_kwargs: {} \n",
       "Creation date: 2025-11-26 15:06:20 \n",
       "Last fit date: 2025-11-26 15:06:46 \n",
       "Index seen by the forecaster: DatetimeIndex(['1992-04-01', '1992-05-01', '1992-06-01', '1992-07-01',\n",
       "               '1992-08-01', '1992-09-01', '1992-10-01', '1992-11-01',\n",
       "               '1992-12-01', '1993-01-01',\n",
       "               ...\n",
       "               '2005-04-01', '2005-05-01', '2005-06-01', '2005-07-01',\n",
       "               '2005-08-01', '2005-09-01', '2005-10-01', '2005-11-01',\n",
       "               '2005-12-01', '2006-01-01'],\n",
       "              dtype='datetime64[ns]', name='datetime', length=166, freq='MS') \n",
       "Skforecast version: 0.19.0 \n",
       "Python version: 3.12.11 \n",
       "Forecaster id: None "
      ]
     },
     "execution_count": 23,
     "metadata": {},
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    }
   ],
   "source": [
    "forecaster"
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   "source": [
    "## Prediction on training data (In-sample Predictions)\n",
    "\n",
    "Predictions on the training data are crucial for evaluating the accuracy and effectiveness of the model. By comparing the predicted values wtih the actual observed values in the training dataset, you can assess how well the model has learned the underlying patterns and trends in the data. This comparison helps in understanding the model's performance and identify areas where it may need improvement or adjustment. In essence, they act as a mirror, reflecting how the model interprets and reconstructs the historical data on which it was trained.\n",
    "\n",
    "The [predictions of the fitted values](https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAXResults.fittedvalues.html#statsmodels.tsa.statespace.sarimax.SARIMAXResults.fittedvalues) are stored in the `fittedvalues` attribute of the `SARIMAXResults` object. This object is stored within the `sarimax_res` attribute of the [skforecast `Sarimax` model](../api/sarimax):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
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    {
     "data": {
      "text/plain": [
       "datetime\n",
       "1992-04-01    0.000000\n",
       "1992-05-01    0.379808\n",
       "1992-06-01    0.361364\n",
       "1992-07-01    0.413265\n",
       "1992-08-01    0.481084\n",
       "                ...   \n",
       "2005-02-01    0.739315\n",
       "2005-03-01    0.751082\n",
       "2005-04-01    0.716096\n",
       "2005-05-01    0.749931\n",
       "2005-06-01    0.817595\n",
       "Freq: MS, Length: 159, dtype: float64"
      ]
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     "execution_count": 24,
     "metadata": {},
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   "source": [
    "# Create and fit ForecasterStats (skforecast)\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterStats(\n",
    "                 estimator=Sarimax(order=(12, 1, 1), maxiter=200),\n",
    "             )\n",
    "forecaster.fit(y=data_train['y'], suppress_warnings=True)\n",
    "\n",
    "# In-sample Predictions\n",
    "# ==============================================================================\n",
    "forecaster.estimator.sarimax_res.fittedvalues"
   ]
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